diff --git a/.gitmodules b/.gitmodules index 5373ec05b3..7f3510b9d6 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ [submodule "source/lib/src/cuda/cub"] path = source/lib/src/cuda/cub - url = git://github.com/NVIDIA/cub.git + url = https://github.com/NVIDIA/cub.git diff --git a/README_Paddle.md b/README_Paddle.md new file mode 100644 index 0000000000..896eb57cf7 --- /dev/null +++ b/README_Paddle.md @@ -0,0 +1,122 @@ +# 1.Introduction +This repo is based on the PaddlePaddle deep learning framework including training and inference parts，DeepMD-kit package，LAMMPS software. The target is, basing on PaddlePaddle framework, to accomplish molecular dynamics simulation with deep learning method. +- PaddlePaddle (PArallel Distributed Deep LEarning) is a simple, efficient and extensible deep learning framework. +- 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). +- LAMMPS is a classical molecular dynamics code with a focus on materials modeling. It's an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. + +# 2.Progress&Features +- Based on Intel CPU, the pipline of training and inference runs smoothly +- Support traditional molecular dynamics software LAMMPS +- Support se_a desciptor model + +# 3.Compiling&Building&Installation +- prepare docker and python environment +``` +docker pull paddlepaddle/paddle:latest-dev-cuda11.0-cudnn8-gcc82 +docker run -it --name {name} -v 绝对路径开发目录:绝对路径开发目录 -v /root/.cache:/root/.cache -v /root/.ccache:/root/.ccache {image_id} bash +rm -f /usr/bin/python3 +ln -s /usr/bin/python3.8 /usr/bin/python3 +wget https://github.com/Kitware/CMake/releases/download/v3.21.0/cmake-3.21.0-linux-x86_64.tar.gz && tar -xf cmake-3.21.0-linux-x86_64.tar.gz +add ~/.bashrc：export PATH=/home/cmake-3.21.0-linux-x86_64/bin:$PATH +``` + +- compile Paddle +``` +git clone https://github.com/PaddlePaddle/Paddle.git +cd Paddle +git reset --hard eca6638c599591c69fe40aa196f5fd42db7efbe2 +rm -rf build && mkdir build && cd build +cmake .. -DPY_VERSION=3.8 -DPYTHON_INCLUDE_DIR=$(python3 -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())") -DPYTHON_LIBRARY=$(python3 -c "import distutils.sysconfig as sysconfig; print(sysconfig.get_config_var('LIBDIR'))") -DWITH_GPU=OFF -DWITH_AVX=ON -DON_INFER=ON -DCMAKE_BUILD_TYPE=Release +# cmake .. -DPY_VERSION=3.8 -DPYTHON_INCLUDE_DIR=$(python3 -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())") -DPYTHON_LIBRARY=$(python3 -c "import distutils.sysconfig as sysconfig; print(sysconfig.get_config_var('LIBDIR'))") -DWITH_GPU=On -DWITH_AVX=ON -DON_INFER=ON -DCUDA_ARCH_NAME=Auto -DCMAKE_BUILD_TYPE=Release +make -j 32 +make -j 32 inference_lib_dist +python3 -m pip install python/dist/paddlepaddle-0.0.0-cp38-cp38-linux_x86_64.whl --no-cache-dir +PADDLE_ROOT=/home/Paddle/build/paddle_inference_install_dir(or add in bashrc with export) +``` +- compile Paddle_DeepMD-kit --training part +``` +cd /home +git clone https://github.com/X4Science/paddle-deepmd.git +cd /home/paddle-deepmd +python3 -m pip install tensorflow-cpu==2.5.0 +# python3 -m pip install tensorflow-gpu==2.5.0 +python3 -m pip install scikit-build +python3 setup.py install +find the package name of deepmd-kit in the location of installation and add in bashrc + export LD_LIBRARY_PATH=/usr/local/lib/python3.8/dist-packages/**{deepmd_name}**/deepmd/op:$LD_LIBRARY_PATH + export LIBRARY_PATH=/usr/local/lib/python3.8/dist-packages/**{deepmd_name}**/deepmd/op:$LIBRARY_PATH + export DEEP_MD_PATH=/usr/local/lib/python3.8/dist-packages/**{deepmd_name}**/deepmd/op +source ~/.bashrc +cd deepmd && python3 load_paddle_op.py install +``` + +- compile Paddle_DeepMD-kit --inference part +``` +rm -rf /home/deepmdroot/ && mkdir /home/deepmdroot && DEEPMD_ROOT=/home/deepmdroot(or add in bashrc with export) +cd /home/paddle-deepmd/source && rm -rf build && mkdir build && cd build +cmake -DCMAKE_INSTALL_PREFIX=$DEEPMD_ROOT -DPADDLE_ROOT=$paddle_root -DUSE_CUDA_TOOLKIT=FALSE -DFLOAT_PREC=low .. +make -j 4 && make install +make lammps +``` +- compile LAMMPS +``` +cd /home +wget https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.7.tar.gz +tar xf openmpi-4.0.7.tar.gz +cd openmpi-4.0.7 +./configure +make all install +add bashrc: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib +#apt install libc-dev +cd /home +wget https://github.com/lammps/lammps/archive/stable_29Oct2020.tar.gz +rm -rf lammps-stable_29Oct2020/ +tar -xzvf stable_29Oct2020.tar.gz +cd lammps-stable_29Oct2020/src/ +cp -r /home/paddle-deepmd/source/build/USER-DEEPMD . +make yes-kspace yes-user-deepmd +#make serial -j 20 +make mpi -j 20 +add in bashrc by + export LD_LIBRARY_PATH=/home/Paddle/build/paddle_inference_install_dir/paddle/lib:$LD_LIBRARY_PATH + export LD_LIBRARY_PATH=/home/Paddle/build/paddle_inference_install_dir/third_party/install/mkldnn/lib:$LD_LIBRARY_PATH + export LD_LIBRARY_PATH=/home/Paddle/build/paddle_inference_install_dir/third_party/install/mklml/lib:$LD_LIBRARY_PATH + export LD_LIBRARY_PATH=/home/Paddle/build/paddle/fluid/pybind/:$LD_LIBRARY_PATH + export LD_LIBRARY_PATH=/home/deepmd-kit/source/build:$LD_LIBRARY_PATH +source ~/.bashrc +``` +# 4.Using Guide +example: water +- training +``` +cd /paddle_deepmd-kit_PATH/example/water/train/ +dp train water_se_a.json +cp ./model.ckpt/model.pd* ../lmp/ -r +cd ../lmp +``` +- inference +``` +mpirun -np 10 lmp_mpi -in in.lammps +``` + + +# 5.Performance +- The performance of inference based on the LAMMPS with PaddlePaddle framework，comparing with TensorFlow framework, about single core and multi-threads +![截屏2022-05-25 23 08 11](https://user-images.githubusercontent.com/50223303/170295703-32e18058-aff9-4368-93cd-38a1ed787e8a.png) +- single thread performance +``` +TF_INTRA_OP_PARALLELISM_THREADS=8 TF_INTER_OP_PARALLELISM_THREADS=1 numactl -c 0 -m 0 lmp_serial -in in.lammps +``` +- multithreads performance +``` +OMP_NUM_THREADS=1 TF_INTRA_OP_PARALLELISM_THREADS=1 TF_INTER_OP_PARALLELISM_THREADS=1 mpirun --allow-run-as-root -np 4 lmp_mpi -in in.lammps +``` +# 6.Future Plans +- fix training precision +- support Gromacs +- support more descriptor and fitting net model +- support GPU trainning + +# 7.Cooperation +Welcome to join us to develop this program together. +Please contact us from [X4Science](https://github.com/X4Science) [PaddlePaddle](https://www.paddlepaddle.org.cn) [PPSIG](https://www.paddlepaddle.org.cn/sig) [PaddleAIforScience](https://www.paddlepaddle.org.cn/science) [PaddleScience](https://github.com/PaddlePaddle/PaddleScience). diff --git a/deepmd/load_paddle_op.py b/deepmd/load_paddle_op.py index d33d0b0914..9370a4d17d 100644 --- a/deepmd/load_paddle_op.py +++ b/deepmd/load_paddle_op.py @@ -1,17 +1,14 @@ -from paddle.utils.cpp_extension import CppExtension, CUDAExtension, setup +from paddle.utils.cpp_extension import CppExtension, setup import site site_package_dir = site.getsitepackages()[0] setup( name='paddle_ops', - ext_modules=CUDAExtension( + ext_modules=CppExtension( sources=['../source/op/paddle_ops/srcs/pd_prod_env_mat_multi_devices_cpu.cc', - '../source/op/paddle_ops/srcs/pd_prod_env_mat_multi_devices_cuda.cc', '../source/op/paddle_ops/srcs/pd_prod_force_se_a_multi_devices_cpu.cc', - '../source/op/paddle_ops/srcs/pd_prod_force_se_a_multi_devices_cuda.cc', - '../source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cpu.cc', - '../source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cuda.cc'], + '../source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cpu.cc'], include_dirs=["../source/lib/include/"], library_dirs=[site_package_dir+"/deepmd/op"], extra_link_args=["-ldeepmd"] diff --git a/examples/water/ipi/water.json b/examples/water/ipi/water.json deleted file mode 100644 index db5b00423e..0000000000 --- a/examples/water/ipi/water.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "verbose": false, - "use_unix": true, - "port": 31415, - "host": "localhost", - "graph_file": "graph.pb", - "coord_file": "conf.xyz", - "atom_type" : { - "OW": 0, - "HW1": 1, - "HW2": 1 - } -} diff --git a/examples/water/lmp/in.lammps b/examples/water/lmp/in.lammps index a501a5ab27..c130203036 100644 --- a/examples/water/lmp/in.lammps +++ b/examples/water/lmp/in.lammps @@ -1,5 +1,5 @@ # bulk water - +echo screen units metal boundary p p p atom_style atomic @@ -11,11 +11,13 @@ read_data water.lmp mass 1 16 mass 2 2 -pair_style deepmd frozen_model.pb +pair_style deepmd model.ckpt/model.pdmodel model.ckpt/model.pdiparams pair_coeff velocity all create 330.0 23456789 +duplicate 10 10 10 +write_data init.data fix 1 all nvt temp 330.0 330.0 0.5 timestep 0.0005 thermo_style custom step pe ke etotal temp press vol diff --git a/examples/water/lmp/log.cite b/examples/water/lmp/log.cite new file mode 100644 index 0000000000..5169ccf03a --- /dev/null +++ b/examples/water/lmp/log.cite @@ -0,0 +1,19 @@ +This LAMMPS simulation made specific use of work described in the +following references. See https://lammps.sandia.gov/cite.html +for details. + +USER-DEEPMD package: + +based on PaddlePaddle framework and inference@article{Wang_ComputPhysCommun_2018_v228_p178, + author = {Wang, Han and Zhang, Linfeng and Han, Jiequn and E, Weinan}, + doi = {10.1016/j.cpc.2018.03.016}, + url = {https://doi.org/10.1016/j.cpc.2018.03.016}, + year = 2018, + month = {jul}, + publisher = {Elsevier {BV}}, + volume = 228, + journal = {Comput. Phys. Commun.}, + title = {{DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics}}, + pages = {178--184} +} + diff --git a/examples/water/lmp/log.lammps b/examples/water/lmp/log.lammps new file mode 100644 index 0000000000..1ec7ccd568 --- /dev/null +++ b/examples/water/lmp/log.lammps @@ -0,0 +1,1060 @@ +LAMMPS (29 Oct 2020) +# bulk water +echo screen +Reading data file ... + triclinic box = (0.0000000 0.0000000 0.0000000) to (12.444700 12.444700 12.444700) with tilt (0.0000000 0.0000000 0.0000000) + 1 by 2 by 5 MPI processor grid + reading atoms ... + 192 atoms + read_data CPU = 0.005 seconds +Replicating atoms ... + triclinic box = (0.0000000 0.0000000 0.0000000) to (49.778800 49.778800 49.778800) with tilt (0.0000000 0.0000000 0.0000000) + 1 by 2 by 5 MPI processor grid + 12288 atoms + replicate CPU = 0.002 seconds +System init for write_data ... +Neighbor list info ... + update every 10 steps, delay 0 steps, check no + max neighbors/atom: 2000, page size: 100000 + master list distance cutoff = 8 + ghost atom cutoff = 8 + binsize = 4, bins = 13 13 13 + 1 neighbor lists, perpetual/occasional/extra = 1 0 0 + (1) pair deepmd, perpetual + attributes: , newton on + pair build: full/bin/atomonly + stencil: full/bin/3d + bin: standard +Per MPI rank memory allocation (min/avg/max) = 4.951 | 4.960 | 4.969 Mbytes +Step PotEng KinEng TotEng Temp Press Volume + 0 -1916424.2 521.42508 -1915902.8 328.30797 8458.1224 123348.33 + 1 -1916418.8 515.48402 -1915903.3 324.56726 9013.8136 123348.33 + 2 -1916410.3 509.1989 -1915901.1 320.60992 9216.224 123348.33 + 3 -1916407.5 503.47764 -1915904 317.00761 9518.9701 123348.33 + 4 -1916408.5 498.98087 -1915909.5 314.17629 9361.1215 123348.33 + 5 -1916397.1 496.07185 -1915901.1 312.34466 8936.0537 123348.33 + 6 -1916397.6 494.84935 -1915902.7 311.57493 8286.7079 123348.33 + 7 -1916397.2 495.11998 -1915902.1 311.74533 7551.3025 123348.33 + 8 -1916397.6 496.391 -1915901.2 312.54561 6581.0691 123348.33 + 9 -1916403.9 497.95773 -1915905.9 313.53208 5772.0978 123348.33 + 10 -1916400.7 499.07374 -1915901.6 314.23476 4821.6647 123348.33 + 11 -1916402.6 499.20887 -1915903.4 314.31984 3736.2495 123348.33 + 12 -1916404.7 498.18543 -1915906.5 313.67545 2989.5282 123348.33 + 13 -1916398.8 496.31052 -1915902.5 312.49494 2297.4532 123348.33 + 14 -1916395.2 494.24232 -1915901 311.19272 1803.785 123348.33 + 15 -1916395.8 492.64622 -1915903.2 310.18776 1638.6004 123348.33 + 16 -1916396.3 492.09158 -1915904.2 309.83854 1669.983 123348.33 + 17 -1916394.1 492.80779 -1915901.3 310.28949 1958.1138 123348.33 + 18 -1916395.1 494.62217 -1915900.4 311.43189 2424.9558 123348.33 + 19 -1916399.8 497.05957 -1915902.8 312.96656 2946.0662 123348.33 + 20 -1916402.1 499.4389 -1915902.7 314.46468 3767.9071 123348.33 + 21 -1916405.5 501.17971 -1915904.3 315.56075 4549.7069 123348.33 + 22 -1916404 502.07386 -1915901.9 316.12374 5538.6882 123348.33 + 23 -1916404.5 502.41028 -1915902.1 316.33556 6415.0498 123348.33 + 24 -1916403.6 502.88044 -1915900.7 316.6316 7325.1519 123348.33 + 25 -1916406.7 504.14012 -1915902.6 317.42473 8201.8116 123348.33 + 26 -1916410.1 506.34605 -1915903.7 318.81367 8930.8776 123348.33 + 27 -1916415.9 508.94963 -1915906.9 320.45298 9344.1466 123348.33 + 28 -1916414.6 510.89854 -1915903.7 321.68008 9713.1196 123348.33 + 29 -1916415.2 511.09931 -1915904.2 321.80649 9783.2207 123348.33 + 30 -1916409.4 508.98231 -1915900.4 320.47355 9789.3403 123348.33 + 31 -1916403.5 504.96318 -1915898.6 317.94296 9566.6329 123348.33 + 32 -1916403.4 500.43996 -1915902.9 315.09498 9074.0286 123348.33 + 33 -1916401.2 497.23387 -1915904 313.07631 8514.8972 123348.33 + 34 -1916400.4 496.87524 -1915903.5 312.85051 7742.2702 123348.33 + 35 -1916401.1 499.99149 -1915901.1 314.81261 6813.6019 123348.33 + 36 -1916411.8 506.13633 -1915905.7 318.68162 5727.179 123348.33 + 37 -1916419.2 514.12207 -1915905 323.70972 4567.5312 123348.33 + 38 -1916424.7 522.4266 -1915902.3 328.93856 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518.1198 -1915901.1 326.22684 5308.9358 123348.33 + 987 -1916410.6 513.15547 -1915897.4 323.10112 5153.407 123348.33 + 988 -1916409.9 509.18652 -1915900.7 320.60213 4927.8016 123348.33 + 989 -1916405.9 506.83221 -1915899.1 319.11977 4544.2275 123348.33 + 990 -1916404 506.7167 -1915897.3 319.04704 4112.4974 123348.33 + 991 -1916411.5 509.32116 -1915902.1 320.6869 3835.7401 123348.33 + 992 -1916414.2 514.47135 -1915899.7 323.92965 3511.4723 123348.33 + 993 -1916421.3 521.02956 -1915900.3 328.05893 3472.167 123348.33 + 994 -1916427.1 527.08619 -1915900 331.8724 3323.5697 123348.33 + 995 -1916427 530.61951 -1915896.4 334.09711 3525.4043 123348.33 + 996 -1916428.5 530.19967 -1915898.3 333.83276 3757.3597 123348.33 + 997 -1916422.5 525.32913 -1915897.2 330.76609 4223.8778 123348.33 + 998 -1916413.3 516.64251 -1915896.7 325.29668 4826.7956 123348.33 + 999 -1916401.5 505.70634 -1915895.8 318.41088 5314.1818 123348.33 + 1000 -1916393.6 494.57045 -1915899 311.39933 5962.5913 123348.33 +Loop time of 2583.19 on 10 procs for 1000 steps with 12288 atoms + +Performance: 0.017 ns/day, 1435.106 hours/ns, 0.387 timesteps/s +96.4% CPU use with 10 MPI tasks x no OpenMP threads + +MPI task timing breakdown: +Section | min time | avg time | max time |%varavg| %total +--------------------------------------------------------------- +Pair | 2352.2 | 2391.4 | 2428.2 | 58.6 | 92.57 +Neigh | 0.89907 | 0.92131 | 0.95856 | 2.0 | 0.04 +Comm | 153.16 | 189.99 | 229.11 | 207.8 | 7.35 +Output | 0.60625 | 0.62791 | 0.64954 | 1.8 | 0.02 +Modify | 0.2462 | 0.28151 | 0.32007 | 4.2 | 0.01 +Other | | 0.01424 | | | 0.00 + +Nlocal: 1228.80 ave 1248 max 1216 min +Histogram: 2 0 4 0 0 2 0 0 0 2 +Nghost: 5714.80 ave 5820 max 5673 min +Histogram: 6 0 2 0 0 0 0 0 0 2 +Neighs: 0.00000 ave 0 max 0 min +Histogram: 10 0 0 0 0 0 0 0 0 0 + +Total # of neighbors = 0 +Ave neighs/atom = 0.0000000 +Neighbor list builds = 100 +Dangerous builds not checked + +Please see the log.cite file for references relevant to this simulation + +Total wall time: 0:43:11 diff --git a/examples/water/train/lcurve.out b/examples/water/train/lcurve.out new file mode 100644 index 0000000000..8c01d2d04d --- /dev/null +++ b/examples/water/train/lcurve.out @@ -0,0 +1,10227 @@ +# batch rmse_tst rmse_trn rmse_e_tst rmse_e_trn rmse_f_tst rmse_f_trn lr + 0 2.68e+01 2.64e+01 1.39e+00 1.39e+00 8.42e-01 8.31e-01 1.0e-03 + 100 2.23e+01 2.03e+01 2.70e-02 2.65e-02 7.04e-01 6.42e-01 1.0e-03 + 200 1.39e+01 1.41e+01 5.46e-01 5.53e-01 4.38e-01 4.44e-01 1.0e-03 + 300 1.26e+01 1.21e+01 1.07e-01 1.05e-01 4.00e-01 3.83e-01 1.0e-03 + 400 9.02e+00 8.96e+00 5.41e-02 5.58e-02 2.85e-01 2.83e-01 1.0e-03 + 500 7.52e+00 6.93e+00 1.61e-01 1.63e-01 2.37e-01 2.19e-01 1.0e-03 + 600 7.05e+00 7.44e+00 3.22e-02 3.53e-02 2.23e-01 2.35e-01 1.0e-03 + 700 7.82e+00 7.38e+00 8.82e-02 8.79e-02 2.47e-01 2.33e-01 1.0e-03 + 800 6.68e+00 6.86e+00 4.40e-03 3.85e-03 2.11e-01 2.17e-01 1.0e-03 + 900 6.52e+00 6.77e+00 8.28e-02 8.08e-02 2.06e-01 2.14e-01 1.0e-03 + 1000 6.89e+00 6.74e+00 4.08e-02 3.84e-02 2.18e-01 2.13e-01 1.0e-03 + 1100 7.66e+00 7.20e+00 2.64e-02 2.28e-02 2.42e-01 2.28e-01 1.0e-03 + 1200 6.11e+00 6.25e+00 3.69e-02 3.66e-02 1.93e-01 1.98e-01 1.0e-03 + 1300 5.84e+00 5.78e+00 3.58e-02 3.56e-02 1.85e-01 1.83e-01 1.0e-03 + 1400 5.90e+00 5.64e+00 1.31e-02 1.19e-02 1.87e-01 1.78e-01 1.0e-03 + 1500 5.52e+00 5.66e+00 3.31e-02 3.06e-02 1.75e-01 1.79e-01 1.0e-03 + 1600 5.51e+00 5.09e+00 2.10e-02 1.91e-02 1.74e-01 1.61e-01 1.0e-03 + 1700 6.13e+00 6.06e+00 2.16e-03 1.96e-03 1.94e-01 1.92e-01 1.0e-03 + 1800 5.03e+00 4.90e+00 9.82e-03 8.39e-03 1.59e-01 1.55e-01 1.0e-03 + 1900 5.96e+00 5.44e+00 2.22e-02 2.43e-02 1.88e-01 1.72e-01 1.0e-03 + 2000 4.87e+00 4.66e+00 4.27e-03 1.58e-03 1.54e-01 1.47e-01 1.0e-03 + 2100 4.88e+00 4.66e+00 6.83e-03 6.23e-03 1.54e-01 1.47e-01 1.0e-03 + 2200 4.76e+00 4.64e+00 3.71e-02 3.76e-02 1.51e-01 1.47e-01 1.0e-03 + 2300 4.67e+00 4.56e+00 1.88e-02 2.09e-02 1.48e-01 1.44e-01 1.0e-03 + 2400 4.72e+00 4.88e+00 8.15e-03 8.36e-03 1.49e-01 1.54e-01 1.0e-03 + 2500 4.50e+00 4.50e+00 7.40e-02 7.49e-02 1.42e-01 1.42e-01 1.0e-03 + 2600 4.46e+00 3.79e+00 1.49e-02 1.57e-02 1.41e-01 1.20e-01 1.0e-03 + 2700 4.52e+00 3.98e+00 1.74e-03 1.48e-03 1.43e-01 1.26e-01 1.0e-03 + 2800 4.33e+00 4.18e+00 2.34e-02 2.53e-02 1.37e-01 1.32e-01 1.0e-03 + 2900 4.60e+00 4.70e+00 3.03e-02 3.04e-02 1.45e-01 1.49e-01 1.0e-03 + 3000 4.64e+00 4.36e+00 1.44e-02 1.54e-02 1.47e-01 1.38e-01 1.0e-03 + 3100 4.16e+00 4.18e+00 1.10e-02 8.67e-03 1.31e-01 1.32e-01 1.0e-03 + 3200 5.61e+00 5.41e+00 3.06e-02 2.77e-02 1.78e-01 1.71e-01 1.0e-03 + 3300 4.24e+00 4.77e+00 1.09e-02 1.05e-02 1.34e-01 1.51e-01 1.0e-03 + 3400 4.07e+00 3.65e+00 2.87e-03 2.64e-03 1.29e-01 1.15e-01 1.0e-03 + 3500 4.11e+00 3.87e+00 2.84e-02 2.96e-02 1.30e-01 1.22e-01 1.0e-03 + 3600 4.67e+00 4.35e+00 2.36e-02 2.18e-02 1.48e-01 1.38e-01 1.0e-03 + 3700 4.44e+00 3.80e+00 2.69e-02 2.69e-02 1.40e-01 1.20e-01 1.0e-03 + 3800 4.16e+00 3.86e+00 1.38e-02 1.22e-02 1.32e-01 1.22e-01 1.0e-03 + 3900 4.03e+00 3.96e+00 3.40e-02 3.40e-02 1.28e-01 1.25e-01 1.0e-03 + 4000 3.92e+00 3.59e+00 2.51e-02 2.61e-02 1.24e-01 1.14e-01 1.0e-03 + 4100 3.98e+00 3.65e+00 3.73e-02 3.84e-02 1.26e-01 1.15e-01 1.0e-03 + 4200 4.21e+00 3.98e+00 2.65e-02 2.42e-02 1.33e-01 1.26e-01 1.0e-03 + 4300 3.91e+00 3.97e+00 2.65e-03 8.95e-04 1.24e-01 1.26e-01 1.0e-03 + 4400 3.73e+00 3.88e+00 1.67e-02 1.60e-02 1.18e-01 1.23e-01 1.0e-03 + 4500 3.89e+00 3.80e+00 6.66e-03 7.34e-03 1.23e-01 1.20e-01 1.0e-03 + 4600 3.68e+00 3.61e+00 1.93e-02 2.05e-02 1.16e-01 1.14e-01 1.0e-03 + 4700 4.32e+00 3.78e+00 1.78e-02 1.81e-02 1.36e-01 1.20e-01 1.0e-03 + 4800 4.15e+00 3.99e+00 1.04e-02 1.13e-02 1.31e-01 1.26e-01 1.0e-03 + 4900 3.65e+00 3.56e+00 1.13e-02 1.02e-02 1.15e-01 1.12e-01 1.0e-03 + 5000 3.53e+00 3.48e+00 1.88e-02 2.01e-02 1.15e-01 1.13e-01 9.5e-04 + 5100 3.63e+00 3.59e+00 2.19e-02 2.19e-02 1.18e-01 1.16e-01 9.5e-04 + 5200 3.47e+00 3.38e+00 5.01e-02 5.12e-02 1.12e-01 1.10e-01 9.5e-04 + 5300 3.43e+00 3.38e+00 2.32e-02 2.11e-02 1.11e-01 1.10e-01 9.5e-04 + 5400 3.51e+00 3.55e+00 1.58e-02 1.40e-02 1.14e-01 1.15e-01 9.5e-04 + 5500 3.76e+00 4.03e+00 7.60e-03 7.42e-03 1.22e-01 1.31e-01 9.5e-04 + 5600 3.29e+00 3.23e+00 4.28e-03 3.34e-03 1.07e-01 1.05e-01 9.5e-04 + 5700 3.32e+00 3.22e+00 3.99e-02 4.07e-02 1.08e-01 1.04e-01 9.5e-04 + 5800 3.47e+00 3.25e+00 4.08e-03 4.50e-03 1.13e-01 1.05e-01 9.5e-04 + 5900 3.52e+00 3.16e+00 2.77e-03 1.45e-03 1.14e-01 1.02e-01 9.5e-04 + 6000 3.26e+00 3.26e+00 7.90e-03 7.08e-03 1.06e-01 1.06e-01 9.5e-04 + 6100 3.20e+00 3.41e+00 2.32e-02 2.19e-02 1.04e-01 1.11e-01 9.5e-04 + 6200 3.40e+00 3.00e+00 4.84e-02 4.91e-02 1.10e-01 9.72e-02 9.5e-04 + 6300 3.11e+00 3.00e+00 1.01e-02 9.48e-03 1.01e-01 9.72e-02 9.5e-04 + 6400 3.56e+00 3.45e+00 5.83e-02 5.83e-02 1.15e-01 1.12e-01 9.5e-04 + 6500 3.06e+00 3.20e+00 1.34e-02 1.48e-02 9.93e-02 1.04e-01 9.5e-04 + 6600 3.03e+00 3.00e+00 3.34e-02 3.29e-02 9.82e-02 9.72e-02 9.5e-04 + 6700 2.91e+00 3.04e+00 4.22e-03 2.83e-03 9.43e-02 9.85e-02 9.5e-04 + 6800 3.11e+00 2.87e+00 1.35e-02 1.33e-02 1.01e-01 9.31e-02 9.5e-04 + 6900 3.41e+00 3.30e+00 1.24e-02 1.25e-02 1.10e-01 1.07e-01 9.5e-04 + 7000 2.79e+00 2.55e+00 3.45e-02 3.54e-02 9.05e-02 8.25e-02 9.5e-04 + 7100 3.28e+00 3.45e+00 2.45e-02 2.45e-02 1.06e-01 1.12e-01 9.5e-04 + 7200 3.45e+00 3.33e+00 3.13e-02 3.06e-02 1.12e-01 1.08e-01 9.5e-04 + 7300 2.78e+00 2.38e+00 3.41e-02 3.45e-02 9.02e-02 7.70e-02 9.5e-04 + 7400 3.56e+00 3.42e+00 2.22e-02 2.34e-02 1.15e-01 1.11e-01 9.5e-04 + 7500 2.77e+00 2.72e+00 4.42e-02 4.48e-02 8.97e-02 8.80e-02 9.5e-04 + 7600 2.72e+00 2.78e+00 1.05e-02 9.05e-03 8.83e-02 9.01e-02 9.5e-04 + 7700 2.74e+00 2.75e+00 7.58e-03 6.96e-03 8.89e-02 8.94e-02 9.5e-04 + 7800 2.93e+00 2.69e+00 1.09e-03 1.83e-04 9.50e-02 8.73e-02 9.5e-04 + 7900 3.15e+00 2.99e+00 1.46e-02 1.36e-02 1.02e-01 9.71e-02 9.5e-04 + 8000 2.75e+00 2.66e+00 3.63e-02 3.57e-02 8.91e-02 8.63e-02 9.5e-04 + 8100 2.75e+00 2.77e+00 1.01e-03 3.05e-04 8.93e-02 8.99e-02 9.5e-04 + 8200 2.86e+00 2.85e+00 1.62e-02 1.55e-02 9.27e-02 9.23e-02 9.5e-04 + 8300 2.82e+00 2.71e+00 1.38e-02 1.43e-02 9.14e-02 8.77e-02 9.5e-04 + 8400 2.68e+00 2.65e+00 6.04e-03 6.23e-03 8.70e-02 8.58e-02 9.5e-04 + 8500 2.60e+00 2.54e+00 3.42e-02 3.40e-02 8.44e-02 8.24e-02 9.5e-04 + 8600 2.62e+00 2.50e+00 7.06e-03 6.58e-03 8.50e-02 8.12e-02 9.5e-04 + 8700 2.72e+00 2.64e+00 7.48e-03 7.89e-03 8.82e-02 8.57e-02 9.5e-04 + 8800 2.59e+00 2.44e+00 1.83e-02 1.89e-02 8.40e-02 7.93e-02 9.5e-04 + 8900 2.75e+00 2.70e+00 3.25e-02 3.49e-02 8.92e-02 8.74e-02 9.5e-04 + 9000 2.56e+00 2.75e+00 1.06e-01 1.06e-01 8.20e-02 8.84e-02 9.5e-04 + 9100 3.32e+00 3.18e+00 3.33e-02 3.39e-02 1.08e-01 1.03e-01 9.5e-04 + 9200 2.56e+00 2.29e+00 1.54e-02 1.43e-02 8.31e-02 7.42e-02 9.5e-04 + 9300 2.71e+00 3.59e+00 2.78e-02 2.82e-02 8.78e-02 1.17e-01 9.5e-04 + 9400 2.87e+00 2.60e+00 5.02e-02 4.98e-02 9.30e-02 8.43e-02 9.5e-04 + 9500 2.96e+00 2.83e+00 4.43e-02 4.30e-02 9.60e-02 9.16e-02 9.5e-04 + 9600 2.67e+00 2.64e+00 1.25e-02 1.20e-02 8.66e-02 8.58e-02 9.5e-04 + 9700 2.47e+00 2.35e+00 2.39e-02 2.37e-02 8.02e-02 7.63e-02 9.5e-04 + 9800 2.79e+00 2.80e+00 2.09e-02 2.12e-02 9.06e-02 9.08e-02 9.5e-04 + 9900 2.42e+00 2.52e+00 1.71e-02 1.80e-02 7.86e-02 8.17e-02 9.5e-04 + 10000 2.64e+00 2.67e+00 1.45e-03 2.25e-03 8.77e-02 8.89e-02 9.0e-04 + 10100 2.50e+00 2.74e+00 5.78e-04 1.67e-03 8.31e-02 9.12e-02 9.0e-04 + 10200 2.30e+00 2.21e+00 1.65e-02 1.74e-02 7.64e-02 7.35e-02 9.0e-04 + 10300 2.46e+00 2.51e+00 1.29e-02 1.13e-02 8.19e-02 8.35e-02 9.0e-04 + 10400 2.32e+00 2.32e+00 7.98e-03 7.75e-03 7.73e-02 7.74e-02 9.0e-04 + 10500 2.23e+00 2.25e+00 6.40e-03 6.32e-03 7.41e-02 7.50e-02 9.0e-04 + 10600 2.77e+00 2.68e+00 1.09e-02 1.04e-02 9.23e-02 8.92e-02 9.0e-04 + 10700 2.35e+00 2.34e+00 1.47e-02 1.54e-02 7.83e-02 7.80e-02 9.0e-04 + 10800 2.71e+00 2.66e+00 9.91e-03 1.14e-02 9.02e-02 8.87e-02 9.0e-04 + 10900 2.30e+00 2.37e+00 2.67e-02 2.80e-02 7.65e-02 7.87e-02 9.0e-04 + 11000 3.28e+00 3.13e+00 1.57e-02 1.70e-02 1.09e-01 1.04e-01 9.0e-04 + 11100 3.06e+00 3.16e+00 5.97e-03 6.55e-03 1.02e-01 1.05e-01 9.0e-04 + 11200 2.60e+00 2.53e+00 4.74e-02 4.81e-02 8.64e-02 8.39e-02 9.0e-04 + 11300 2.30e+00 2.28e+00 3.94e-02 3.98e-02 7.64e-02 7.58e-02 9.0e-04 + 11400 2.50e+00 2.52e+00 4.21e-02 4.21e-02 8.28e-02 8.36e-02 9.0e-04 + 11500 2.35e+00 2.33e+00 1.19e-02 1.25e-02 7.82e-02 7.74e-02 9.0e-04 + 11600 2.57e+00 2.33e+00 2.36e-02 2.35e-02 8.55e-02 7.74e-02 9.0e-04 + 11700 2.37e+00 2.13e+00 2.93e-02 2.93e-02 7.88e-02 7.08e-02 9.0e-04 + 11800 2.39e+00 2.46e+00 5.42e-03 6.99e-03 7.94e-02 8.18e-02 9.0e-04 + 11900 2.42e+00 2.15e+00 1.43e-02 1.48e-02 8.04e-02 7.15e-02 9.0e-04 + 12000 2.54e+00 2.43e+00 2.75e-02 2.79e-02 8.46e-02 8.09e-02 9.0e-04 + 12100 2.28e+00 2.30e+00 1.23e-02 1.09e-02 7.59e-02 7.66e-02 9.0e-04 + 12200 2.48e+00 2.45e+00 2.87e-02 2.88e-02 8.23e-02 8.13e-02 9.0e-04 + 12300 2.18e+00 2.17e+00 3.66e-02 3.71e-02 7.24e-02 7.21e-02 9.0e-04 + 12400 2.39e+00 2.06e+00 2.35e-02 2.42e-02 7.95e-02 6.86e-02 9.0e-04 + 12500 2.24e+00 2.18e+00 4.00e-03 3.85e-03 7.45e-02 7.26e-02 9.0e-04 + 12600 2.26e+00 2.15e+00 7.41e-03 8.92e-03 7.51e-02 7.15e-02 9.0e-04 + 12700 2.40e+00 2.28e+00 3.23e-02 3.36e-02 7.96e-02 7.56e-02 9.0e-04 + 12800 2.29e+00 2.26e+00 6.29e-03 5.94e-03 7.62e-02 7.51e-02 9.0e-04 + 12900 2.20e+00 2.29e+00 1.09e-02 1.11e-02 7.33e-02 7.63e-02 9.0e-04 + 13000 2.21e+00 1.88e+00 9.97e-03 9.17e-03 7.35e-02 6.25e-02 9.0e-04 + 13100 2.42e+00 2.35e+00 1.11e-02 1.10e-02 8.06e-02 7.83e-02 9.0e-04 + 13200 2.18e+00 2.04e+00 3.44e-02 3.35e-02 7.24e-02 6.76e-02 9.0e-04 + 13300 2.35e+00 2.11e+00 5.39e-02 5.30e-02 7.76e-02 6.99e-02 9.0e-04 + 13400 2.26e+00 2.41e+00 2.64e-03 2.28e-03 7.54e-02 8.02e-02 9.0e-04 + 13500 2.33e+00 2.51e+00 2.28e-02 2.26e-02 7.74e-02 8.34e-02 9.0e-04 + 13600 2.31e+00 2.09e+00 4.88e-02 4.73e-02 7.66e-02 6.93e-02 9.0e-04 + 13700 2.23e+00 2.19e+00 1.93e-02 1.87e-02 7.40e-02 7.29e-02 9.0e-04 + 13800 2.29e+00 2.18e+00 2.55e-02 2.59e-02 7.61e-02 7.23e-02 9.0e-04 + 13900 2.38e+00 2.43e+00 1.46e-02 1.47e-02 7.90e-02 8.09e-02 9.0e-04 + 14000 2.74e+00 2.59e+00 3.62e-02 3.62e-02 9.11e-02 8.59e-02 9.0e-04 + 14100 2.14e+00 2.05e+00 1.64e-02 1.62e-02 7.13e-02 6.83e-02 9.0e-04 + 14200 2.51e+00 2.41e+00 1.01e-02 1.08e-02 8.37e-02 8.03e-02 9.0e-04 + 14300 2.44e+00 2.26e+00 4.95e-03 5.05e-03 8.11e-02 7.54e-02 9.0e-04 + 14400 2.43e+00 2.31e+00 1.62e-02 1.61e-02 8.07e-02 7.67e-02 9.0e-04 + 14500 2.49e+00 2.35e+00 3.20e-02 3.15e-02 8.29e-02 7.82e-02 9.0e-04 + 14600 2.23e+00 2.22e+00 1.14e-02 1.24e-02 7.42e-02 7.39e-02 9.0e-04 + 14700 2.13e+00 2.44e+00 5.21e-03 4.50e-03 7.10e-02 8.13e-02 9.0e-04 + 14800 2.16e+00 2.19e+00 2.11e-02 2.09e-02 7.17e-02 7.29e-02 9.0e-04 + 14900 2.24e+00 2.17e+00 8.01e-03 7.40e-03 7.47e-02 7.21e-02 9.0e-04 + 15000 2.12e+00 1.97e+00 1.34e-02 1.43e-02 7.24e-02 6.71e-02 8.6e-04 + 15100 2.09e+00 2.12e+00 1.57e-02 1.62e-02 7.14e-02 7.24e-02 8.6e-04 + 15200 2.12e+00 2.34e+00 8.15e-03 8.32e-03 7.22e-02 7.98e-02 8.6e-04 + 15300 2.02e+00 2.13e+00 1.21e-02 1.22e-02 6.90e-02 7.28e-02 8.6e-04 + 15400 2.08e+00 2.16e+00 1.77e-02 1.86e-02 7.09e-02 7.37e-02 8.6e-04 + 15500 2.02e+00 2.06e+00 2.15e-03 1.48e-03 6.91e-02 7.05e-02 8.6e-04 + 15600 2.17e+00 2.28e+00 1.99e-02 1.94e-02 7.40e-02 7.77e-02 8.6e-04 + 15700 2.02e+00 1.92e+00 2.56e-02 2.62e-02 6.88e-02 6.55e-02 8.6e-04 + 15800 2.67e+00 2.49e+00 1.15e-02 1.16e-02 9.11e-02 8.52e-02 8.6e-04 + 15900 2.10e+00 2.01e+00 9.66e-03 9.19e-03 7.16e-02 6.85e-02 8.6e-04 + 16000 1.98e+00 1.93e+00 1.91e-02 1.89e-02 6.74e-02 6.57e-02 8.6e-04 + 16100 2.10e+00 1.92e+00 3.49e-02 3.48e-02 7.13e-02 6.53e-02 8.6e-04 + 16200 1.99e+00 2.07e+00 2.50e-02 2.49e-02 6.78e-02 7.06e-02 8.6e-04 + 16300 2.11e+00 1.88e+00 2.37e-02 2.52e-02 7.19e-02 6.42e-02 8.6e-04 + 16400 2.08e+00 2.13e+00 2.47e-03 1.88e-03 7.12e-02 7.29e-02 8.6e-04 + 16500 2.10e+00 2.02e+00 3.55e-03 3.63e-03 7.17e-02 6.91e-02 8.6e-04 + 16600 2.05e+00 2.01e+00 4.94e-03 5.31e-03 7.01e-02 6.87e-02 8.6e-04 + 16700 2.34e+00 2.23e+00 2.46e-02 2.47e-02 7.96e-02 7.60e-02 8.6e-04 + 16800 2.00e+00 1.89e+00 1.74e-02 1.68e-02 6.82e-02 6.45e-02 8.6e-04 + 16900 2.03e+00 1.99e+00 2.13e-02 2.16e-02 6.91e-02 6.77e-02 8.6e-04 + 17000 1.97e+00 1.80e+00 7.04e-03 6.90e-03 6.73e-02 6.13e-02 8.6e-04 + 17100 2.00e+00 2.06e+00 2.57e-03 3.81e-03 6.81e-02 7.04e-02 8.6e-04 + 17200 1.99e+00 2.07e+00 6.32e-03 5.95e-03 6.78e-02 7.05e-02 8.6e-04 + 17300 1.99e+00 1.99e+00 1.42e-02 1.30e-02 6.80e-02 6.79e-02 8.6e-04 + 17400 2.07e+00 2.10e+00 3.48e-03 3.89e-03 7.07e-02 7.17e-02 8.6e-04 + 17500 2.52e+00 2.39e+00 1.90e-02 1.84e-02 8.61e-02 8.15e-02 8.6e-04 + 17600 2.15e+00 1.90e+00 2.18e-02 2.12e-02 7.33e-02 6.48e-02 8.6e-04 + 17700 2.02e+00 1.89e+00 1.63e-02 1.60e-02 6.89e-02 6.46e-02 8.6e-04 + 17800 2.03e+00 1.92e+00 1.79e-02 1.74e-02 6.91e-02 6.56e-02 8.6e-04 + 17900 1.94e+00 1.79e+00 7.48e-03 7.17e-03 6.61e-02 6.10e-02 8.6e-04 + 18000 2.03e+00 1.99e+00 5.69e-04 1.53e-04 6.92e-02 6.80e-02 8.6e-04 + 18100 2.02e+00 2.00e+00 7.43e-03 6.62e-03 6.91e-02 6.83e-02 8.6e-04 + 18200 1.95e+00 1.93e+00 3.56e-03 4.32e-03 6.67e-02 6.58e-02 8.6e-04 + 18300 2.32e+00 2.63e+00 5.30e-02 5.25e-02 7.86e-02 8.93e-02 8.6e-04 + 18400 2.18e+00 2.06e+00 2.04e-02 1.99e-02 7.43e-02 7.01e-02 8.6e-04 + 18500 1.94e+00 1.69e+00 1.24e-02 1.33e-02 6.61e-02 5.76e-02 8.6e-04 + 18600 1.97e+00 1.78e+00 1.05e-02 1.02e-02 6.71e-02 6.08e-02 8.6e-04 + 18700 2.07e+00 2.06e+00 3.19e-03 2.48e-03 7.06e-02 7.04e-02 8.6e-04 + 18800 1.92e+00 1.82e+00 1.90e-02 1.95e-02 6.54e-02 6.21e-02 8.6e-04 + 18900 2.47e+00 2.38e+00 2.42e-02 2.51e-02 8.44e-02 8.12e-02 8.6e-04 + 19000 1.98e+00 1.84e+00 6.88e-04 7.22e-04 6.77e-02 6.30e-02 8.6e-04 + 19100 1.99e+00 1.88e+00 1.73e-02 1.69e-02 6.78e-02 6.42e-02 8.6e-04 + 19200 2.20e+00 2.12e+00 4.75e-02 4.74e-02 7.46e-02 7.18e-02 8.6e-04 + 19300 1.88e+00 1.85e+00 6.65e-03 6.95e-03 6.41e-02 6.30e-02 8.6e-04 + 19400 2.01e+00 2.03e+00 2.14e-02 2.20e-02 6.84e-02 6.90e-02 8.6e-04 + 19500 2.70e+00 2.55e+00 1.90e-02 1.94e-02 9.20e-02 8.69e-02 8.6e-04 + 19600 1.99e+00 1.90e+00 9.67e-03 1.03e-02 6.79e-02 6.48e-02 8.6e-04 + 19700 1.93e+00 1.72e+00 7.88e-03 9.11e-03 6.59e-02 5.88e-02 8.6e-04 + 19800 1.95e+00 1.83e+00 4.55e-02 4.49e-02 6.61e-02 6.19e-02 8.6e-04 + 19900 2.00e+00 2.02e+00 4.72e-03 5.20e-03 6.82e-02 6.91e-02 8.6e-04 + 20000 1.93e+00 1.75e+00 2.76e-03 2.18e-03 6.75e-02 6.12e-02 8.1e-04 + 20100 1.80e+00 1.76e+00 1.35e-02 1.31e-02 6.29e-02 6.17e-02 8.1e-04 + 20200 1.92e+00 1.92e+00 8.62e-03 8.81e-03 6.72e-02 6.71e-02 8.1e-04 + 20300 1.83e+00 1.86e+00 1.81e-02 1.83e-02 6.41e-02 6.51e-02 8.1e-04 + 20400 1.90e+00 1.95e+00 3.27e-03 3.73e-03 6.65e-02 6.82e-02 8.1e-04 + 20500 1.97e+00 1.97e+00 1.36e-02 1.44e-02 6.89e-02 6.89e-02 8.1e-04 + 20600 1.99e+00 1.93e+00 3.28e-02 3.19e-02 6.94e-02 6.74e-02 8.1e-04 + 20700 2.07e+00 2.04e+00 2.26e-02 2.43e-02 7.25e-02 7.14e-02 8.1e-04 + 20800 1.89e+00 1.86e+00 1.63e-02 1.56e-02 6.61e-02 6.52e-02 8.1e-04 + 20900 1.81e+00 1.74e+00 4.84e-03 3.74e-03 6.33e-02 6.09e-02 8.1e-04 + 21000 1.83e+00 1.80e+00 1.60e-02 1.62e-02 6.41e-02 6.30e-02 8.1e-04 + 21100 1.78e+00 1.66e+00 5.34e-03 5.51e-03 6.24e-02 5.82e-02 8.1e-04 +# batch rmse_tst rmse_trn rmse_e_tst rmse_e_trn rmse_f_tst rmse_f_trn lr + 0 2.68e+01 2.64e+01 1.39e+00 1.39e+00 8.42e-01 8.31e-01 1.0e-03 + 100 1.54e+01 1.40e+01 5.22e-02 5.18e-02 8.07e-01 7.33e-01 3.6e-04 + 200 8.60e+00 8.34e+00 2.82e-02 3.28e-02 7.48e-01 7.24e-01 1.3e-04 + 300 5.07e+00 5.29e+00 5.18e-03 5.21e-03 7.28e-01 7.59e-01 4.8e-05 + 400 3.09e+00 2.83e+00 6.89e-03 1.19e-03 7.23e-01 6.64e-01 1.7e-05 + 500 1.95e+00 1.68e+00 6.98e-03 8.39e-03 7.23e-01 6.24e-01 6.2e-06 + 600 1.31e+00 1.26e+00 5.10e-03 6.07e-03 7.23e-01 6.99e-01 2.3e-06 + 700 9.77e-01 9.55e-01 5.11e-03 7.70e-03 7.23e-01 7.04e-01 8.2e-07 + 800 8.26e-01 8.02e-01 5.05e-03 6.51e-04 7.23e-01 7.05e-01 3.0e-07 + 900 7.64e-01 6.65e-01 5.05e-03 4.79e-03 7.23e-01 6.29e-01 1.1e-07 + 1000 7.40e-01 7.07e-01 5.05e-03 1.81e-03 7.23e-01 6.93e-01 3.9e-08 +# batch rmse_tst rmse_trn rmse_e_tst rmse_e_trn rmse_f_tst rmse_f_trn lr + 0 2.68e+01 2.64e+01 1.39e+00 1.39e+00 8.42e-01 8.31e-01 1.0e-03 + 100 2.23e+01 2.03e+01 2.70e-02 2.65e-02 7.04e-01 6.42e-01 1.0e-03 + 200 1.39e+01 1.41e+01 5.46e-01 5.53e-01 4.38e-01 4.44e-01 1.0e-03 + 300 1.26e+01 1.21e+01 1.07e-01 1.05e-01 4.00e-01 3.83e-01 1.0e-03 + 400 9.02e+00 8.96e+00 5.41e-02 5.58e-02 2.85e-01 2.83e-01 1.0e-03 + 500 7.52e+00 6.93e+00 1.61e-01 1.63e-01 2.37e-01 2.19e-01 1.0e-03 + 600 7.05e+00 7.44e+00 3.22e-02 3.53e-02 2.23e-01 2.35e-01 1.0e-03 + 700 7.82e+00 7.38e+00 8.82e-02 8.79e-02 2.47e-01 2.33e-01 1.0e-03 + 800 6.68e+00 6.86e+00 4.40e-03 3.85e-03 2.11e-01 2.17e-01 1.0e-03 + 900 6.52e+00 6.77e+00 8.28e-02 8.08e-02 2.06e-01 2.14e-01 1.0e-03 + 1000 6.89e+00 6.74e+00 4.08e-02 3.84e-02 2.18e-01 2.13e-01 1.0e-03 + 1100 7.66e+00 7.20e+00 2.64e-02 2.28e-02 2.42e-01 2.28e-01 1.0e-03 + 1200 6.11e+00 6.25e+00 3.69e-02 3.66e-02 1.93e-01 1.98e-01 1.0e-03 + 1300 5.84e+00 5.78e+00 3.58e-02 3.56e-02 1.85e-01 1.83e-01 1.0e-03 + 1400 5.90e+00 5.64e+00 1.31e-02 1.19e-02 1.87e-01 1.78e-01 1.0e-03 + 1500 5.52e+00 5.66e+00 3.31e-02 3.06e-02 1.75e-01 1.79e-01 1.0e-03 + 1600 5.51e+00 5.09e+00 2.10e-02 1.91e-02 1.74e-01 1.61e-01 1.0e-03 + 1700 6.13e+00 6.06e+00 2.16e-03 1.96e-03 1.94e-01 1.92e-01 1.0e-03 + 1800 5.03e+00 4.90e+00 9.82e-03 8.39e-03 1.59e-01 1.55e-01 1.0e-03 + 1900 5.96e+00 5.44e+00 2.22e-02 2.43e-02 1.88e-01 1.72e-01 1.0e-03 + 2000 4.87e+00 4.66e+00 4.27e-03 1.58e-03 1.54e-01 1.47e-01 1.0e-03 + 2100 4.88e+00 4.66e+00 6.83e-03 6.23e-03 1.54e-01 1.47e-01 1.0e-03 + 2200 4.76e+00 4.64e+00 3.71e-02 3.76e-02 1.51e-01 1.47e-01 1.0e-03 + 2300 4.67e+00 4.56e+00 1.88e-02 2.09e-02 1.48e-01 1.44e-01 1.0e-03 + 2400 4.72e+00 4.88e+00 8.15e-03 8.36e-03 1.49e-01 1.54e-01 1.0e-03 + 2500 4.50e+00 4.50e+00 7.40e-02 7.49e-02 1.42e-01 1.42e-01 1.0e-03 + 2600 4.46e+00 3.79e+00 1.49e-02 1.57e-02 1.41e-01 1.20e-01 1.0e-03 + 2700 4.52e+00 3.98e+00 1.74e-03 1.48e-03 1.43e-01 1.26e-01 1.0e-03 + 2800 4.33e+00 4.18e+00 2.34e-02 2.53e-02 1.37e-01 1.32e-01 1.0e-03 + 2900 4.60e+00 4.70e+00 3.03e-02 3.04e-02 1.45e-01 1.49e-01 1.0e-03 + 3000 4.64e+00 4.36e+00 1.44e-02 1.54e-02 1.47e-01 1.38e-01 1.0e-03 + 3100 4.16e+00 4.18e+00 1.10e-02 8.67e-03 1.31e-01 1.32e-01 1.0e-03 + 3200 5.61e+00 5.41e+00 3.06e-02 2.77e-02 1.78e-01 1.71e-01 1.0e-03 + 3300 4.24e+00 4.77e+00 1.09e-02 1.05e-02 1.34e-01 1.51e-01 1.0e-03 + 3400 4.07e+00 3.65e+00 2.87e-03 2.64e-03 1.29e-01 1.15e-01 1.0e-03 + 3500 4.11e+00 3.87e+00 2.84e-02 2.96e-02 1.30e-01 1.22e-01 1.0e-03 + 3600 4.67e+00 4.35e+00 2.36e-02 2.18e-02 1.48e-01 1.38e-01 1.0e-03 + 3700 4.44e+00 3.80e+00 2.69e-02 2.69e-02 1.40e-01 1.20e-01 1.0e-03 + 3800 4.16e+00 3.86e+00 1.38e-02 1.22e-02 1.32e-01 1.22e-01 1.0e-03 + 3900 4.03e+00 3.96e+00 3.40e-02 3.40e-02 1.28e-01 1.25e-01 1.0e-03 + 4000 3.92e+00 3.59e+00 2.51e-02 2.61e-02 1.24e-01 1.14e-01 1.0e-03 + 4100 3.98e+00 3.65e+00 3.73e-02 3.84e-02 1.26e-01 1.15e-01 1.0e-03 + 4200 4.21e+00 3.98e+00 2.65e-02 2.42e-02 1.33e-01 1.26e-01 1.0e-03 + 4300 3.91e+00 3.97e+00 2.65e-03 8.95e-04 1.24e-01 1.26e-01 1.0e-03 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b/examples/water/train/out.json new file mode 100644 index 0000000000..1345c1dd98 --- /dev/null +++ b/examples/water/train/out.json @@ -0,0 +1,88 @@ +{ + "model": { + "type_map": [ + "O", + "H" + ], + "descriptor": { + "type": "se_a", + "sel": [ + 46, + 92 + ], + "rcut_smth": 5.8, + "rcut": 6.0, + "neuron": [ + 25, + 50, + 100 + ], + "resnet_dt": false, + "axis_neuron": 16, + "seed": 1, + "activation_function": "tanh", + "type_one_side": false, + "precision": "float64", + "trainable": true, + "exclude_types": [], + "set_davg_zero": false + }, + "fitting_net": { + "neuron": [ + 240, + 240, + 240 + ], + "resnet_dt": true, + "seed": 1, + "type": "ener", + "numb_fparam": 0, + "numb_aparam": 0, + "activation_function": "tanh", + "precision": "float64", + "trainable": true, + "rcond": 0.001, + "atom_ener": [] + }, + "data_stat_nbatch": 10, + "data_stat_protect": 0.01 + }, + "learning_rate": { + "type": "exp", + "decay_steps": 5000, + "start_lr": 0.001, + "stop_lr": 3.51e-08 + }, + "loss": { + "start_pref_e": 0.02, + "limit_pref_e": 1, + "start_pref_f": 1000, + "limit_pref_f": 1, + "start_pref_v": 0, + "limit_pref_v": 0, + "type": "ener", + "start_pref_ae": 0.0, + "limit_pref_ae": 0.0 + }, + "training": { + "systems": [ + "../data/" + ], + "set_prefix": "set", + "stop_batch": 1000000, + "batch_size": 1, + "seed": 1, + "disp_file": "lcurve.out", + "disp_freq": 100, + "numb_test": 10, + "save_ckpt": "model.ckpt", + "load_ckpt": "model.ckpt", + "disp_training": true, + "time_training": true, + "tensorboard": false, + "tensorboard_log_dir": "log", + "profiling": false, + "profiling_file": "timeline.json", + "save_freq": 10000 + } +} \ No newline at end of file diff --git a/examples/water/train/polar.json b/examples/water/train/polar.json deleted file mode 100644 index 28e82d4587..0000000000 --- a/examples/water/train/polar.json +++ /dev/null @@ -1,64 +0,0 @@ -{ - "with_distrib": false, - "_comment": " model parameters", - "model":{ - "type_map": ["O", "H"], - "data_stat_nbatch": 1, - "descriptor": { - "type": "loc_frame", - "sel_a": [16, 32], - "sel_r": [30, 60], - "rcut": 6.00, - "axis_rule": [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0], - "_comment": " default rule: []", - "_comment": " user defined rule: for each type provides two axes, ", - "_comment": " for each axis: (a_or_r, type, idx)", - "_comment": " if type < 0, exclude type -(type+1)", - "_comment": " for water (O:0, H:1) it can be", - "_comment": " [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0]", - "_comment": " that's all" - }, - "fitting_net": { - "type": "polar", - "sel_type": [0], - "neuron": [100, 100, 100], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" - }, - - "learning_rate" :{ - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-8, - "_comment": "that's all" - }, - - "_comment": " traing controls", - "training": { - "systems": ["/path/to/data/polar/bulk"], - "set_prefix": "set", - "stop_batch": 1000000, - "batch_size": [4], - - "seed": 1, - - "_comment": " display and restart", - "_comment": " frequencies counted in batch", - "disp_file": "lcurve.out", - "disp_freq": 100, - "numb_test": 10, - "save_freq": 1000, - "save_ckpt": "model.ckpt", - "load_ckpt": "model.ckpt", - "disp_training":true, - "time_training":true, - "_comment": "that's all" - }, - - "_comment": "that's all" -} - diff --git a/examples/water/train/polar_se_a.json b/examples/water/train/polar_se_a.json deleted file mode 100644 index 048c12f765..0000000000 --- a/examples/water/train/polar_se_a.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "with_distrib": false, - "_comment": " model parameters", - "model":{ - "type_map": ["O", "H"], - "data_stat_nbatch": 10, - "descriptor" :{ - "type": "se_a", - "sel": [46, 92], - "rcut_smth": 5.80, - "rcut": 6.00, - "neuron": [25, 50, 100], - "resnet_dt": false, - "axis_neuron": 16, - "seed": 1, - "_comment": " that's all" - }, - "fitting_net": { - "type": "polar", - "sel_type": [0], - "fit_diag": false, - "neuron": [100, 100, 100], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" - }, - - "learning_rate" :{ - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.01, - "stop_lr": 3.51e-7, - "_comment": "that's all" - }, - - "_comment": " traing controls", - "training": { - "systems": ["/path/to/data/polar/bulk"], - "set_prefix": "set", - "stop_batch": 1000000, - "batch_size": [1], - - "seed": 1, - - "_comment": " display and restart", - "_comment": " frequencies counted in batch", - "disp_file": "lcurve.out", - "disp_freq": 100, - "numb_test": 10, - "save_freq": 1000, - "save_ckpt": "model.ckpt", - "load_ckpt": "model.ckpt", - "disp_training":true, - "time_training":true, - "_comment": "that's all" - }, - - "_comment": "that's all" -} - diff --git a/examples/water/train/wannier.json b/examples/water/train/wannier.json deleted file mode 100644 index f23f5e0d62..0000000000 --- a/examples/water/train/wannier.json +++ /dev/null @@ -1,65 +0,0 @@ -{ - "with_distrib": false, - "_comment": " model parameters", - "model":{ - "type_map": ["O", "H"], - "data_stat_nbatch": 1, - "descriptor": { - "type": "loc_frame", - "sel_a": [16, 32], - "sel_r": [30, 60], - "rcut": 6.00, - "axis_rule": [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0], - "_comment": " default rule: []", - "_comment": " user defined rule: for each type provides two axes, ", - "_comment": " for each axis: (a_or_r, type, idx)", - "_comment": " if type < 0, exclude type -(type+1)", - "_comment": " for water (O:0, H:1) it can be", - "_comment": " [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0]", - "_comment": " that's all" - }, - "fitting_net": { - "type": "wfc", - "wfc_numb": 4, - "sel_type": [0], - "neuron": [100, 100, 100], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" - }, - - "learning_rate" :{ - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-8, - "_comment": "that's all" - }, - - "_comment": " traing controls", - "training": { - "systems": ["../data.wfc/"], - "set_prefix": "set", - "stop_batch": 1000000, - "batch_size": [4], - - "seed": 1, - - "_comment": " display and restart", - "_comment": " frequencies counted in batch", - "disp_file": "lcurve.out", - "disp_freq": 100, - "numb_test": 10, - "save_freq": 1000, - "save_ckpt": "model.ckpt", - "load_ckpt": "model.ckpt", - "disp_training":true, - "time_training":true, - "_comment": "that's all" - }, - - "_comment": "that's all" -} - diff --git a/examples/water/train/water.json b/examples/water/train/water.json deleted file mode 100644 index 23ba559aed..0000000000 --- a/examples/water/train/water.json +++ /dev/null @@ -1,72 +0,0 @@ -{ - "with_distrib": false, - "_comment": " model parameters", - "model":{ - "type_map": ["O", "H"], - "data_stat_nbatch": 10, - "descriptor": { - "type": "loc_frame", - "sel_a": [16, 32], - "sel_r": [30, 60], - "rcut": 6.00, - "axis_rule": [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0], - "_comment": " default rule: []", - "_comment": " user defined rule: for each type provides two axes, ", - "_comment": " for each axis: (a_or_r, type, idx)", - "_comment": " if type < 0, exclude type -(type+1)", - "_comment": " for water (O:0, H:1) it can be", - "_comment": " [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0]", - "_comment": " that's all" - }, - "fitting_net": { - "neuron": [240, 120, 60, 30, 10], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" - }, - - "learning_rate" :{ - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-8, - "_comment": "that's all" - }, - - "loss" : { - "start_pref_e": 0.02, - "limit_pref_e": 8, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": "that's all" - }, - - "_comment": " traing controls", - "training": { - "systems": ["../data/"], - "set_prefix": "set", - "stop_batch": 1000000, - "batch_size": [4], - - "seed": 1, - - "_comment": " display and restart", - "_comment": " frequencies counted in batch", - "disp_file": "lcurve.out", - "disp_freq": 100, - "numb_test": 10, - "save_freq": 1000, - "save_ckpt": "model.ckpt", - "load_ckpt": "model.ckpt", - "disp_training":true, - "time_training":true, - "_comment": "that's all" - }, - - "_comment": "that's all" -} - diff --git a/examples/water/train/water_se_a.json b/examples/water/train/water_se_a.json index 368170a77f..750c55c6f3 100644 --- a/examples/water/train/water_se_a.json +++ b/examples/water/train/water_se_a.json @@ -54,7 +54,6 @@ "disp_file": "lcurve.out", "disp_freq": 100, "numb_test": 10, - "save_freq": 1000, "save_ckpt": "model.ckpt", "load_ckpt": "model.ckpt", "disp_training":true, diff --git a/examples/water/train/water_se_a.yaml b/examples/water/train/water_se_a.yaml deleted file mode 100644 index b92bf2ab4e..0000000000 --- a/examples/water/train/water_se_a.yaml +++ /dev/null @@ -1,68 +0,0 @@ -# model parameters -model: - type_map: - - O - - H - descriptor: - type: se_a - sel: - - 46 - - 92 - rcut_smth: 5.8 - rcut: 6.0 - neuron: - - 25 - - 50 - - 100 - resnet_dt: false - axis_neuron: 16 - seed: 1 - # that's all for descriptor - fitting_net: - neuron: - - 240 - - 240 - - 240 - resnet_dt: true - seed: 1 - # that's all for fitting net - # that's all for model - -learning_rate: - type: exp - decay_steps: 5000 - start_lr: 0.001 - stop_lr: 3.51e-08 - # that's all for learnnig rate - -loss: - start_pref_e: 0.02 - limit_pref_e: 1 - start_pref_f: 1000 - limit_pref_f: 1 - start_pref_v: 0 - limit_pref_v: 0 - # that's all for loss - -# training contols -training: - systems: - - ../data/ - set_prefix: set - stop_batch: 1000000 - batch_size: 1 - seed: 1 - # display and restart - # frequencies counted in batch - disp_file: lcurve.out - disp_freq: 100 - numb_test: 10 - save_freq: 1000 - save_ckpt: model.ckpt - load_ckpt: model.ckpt - disp_training: true - time_training: true - profiling: false - profiling_file: timeline.json - # that's all for training -# that's all \ No newline at end of file diff --git a/examples/water/train/water_se_ar.json b/examples/water/train/water_se_ar.json deleted file mode 100644 index 2173f2e1d9..0000000000 --- a/examples/water/train/water_se_ar.json +++ /dev/null @@ -1,80 +0,0 @@ -{ - "_comment": " model parameters", - "model": { - "type_map": ["O", "H"], - "descriptor" :{ - "type": "se_ar", - "a" : { - "sel": [20, 40], - "rcut_smth": 3.50, - "rcut": 4.00, - "neuron": [10, 20, 40], - "resnet_dt": false, - "axis_neuron": 4, - "seed": 1, - "_comment": " that's all" - }, - "r": { - "sel": [46, 92], - "rcut_smth": 1.00, - "rcut": 6.00, - "neuron": [5, 10, 20], - "resnet_dt": false, - "seed": 1, - "_comment": " that's all" - } - }, - "fitting_net" : { - "neuron": [240, 240, 240], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" - }, - - "learning_rate" :{ - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.005, - "stop_lr": 1.76e-7, - "_comment": "that's all" - }, - - "loss" :{ - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" - }, - - "_comment": " traing controls", - "training" : { - "systems": ["../data/"], - "set_prefix": "set", - "stop_batch": 1000000, - "batch_size": 1, - - "seed": 1, - - "_comment": " display and restart", - "_comment": " frequencies counted in batch", - "disp_file": "lcurve.out", - "disp_freq": 100, - "numb_test": 10, - "save_freq": 1000, - "save_ckpt": "model.ckpt", - "load_ckpt": "model.ckpt", - "disp_training":true, - "time_training":true, - "profiling": false, - "profiling_file":"timeline.json", - "_comment": "that's all" - }, - - "_comment": "that's all" -} - diff --git a/examples/water/train/water_se_r.json b/examples/water/train/water_se_r.json deleted file mode 100644 index 7faf55a3c3..0000000000 --- a/examples/water/train/water_se_r.json +++ /dev/null @@ -1,69 +0,0 @@ -{ - "_comment": " model parameters", - "model_type": "se_r", - "model": { - "type_map": ["O", "H"], - "descriptor": { - "type": "se_r", - "sel": [46, 92], - "rcut_smth": 1.00, - "rcut": 6.00, - "neuron": [5, 10, 20], - "resnet_dt": false, - "seed": 1, - "_comment": " that's all" - }, - "fitting_net" :{ - "neuron": [120, 120, 120], - "resnet_dt": true, - "seed": 1, - "_comment": "that's all" - }, - "_comment": " that's all" - }, - - "learning_rate" : { - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.005, - "stop_lr": 1.76e-7, - "_comment": " that's all" - }, - - "loss" : { - "start_pref_e": 0.02, - "limit_pref_e": 1, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" - }, - - "_comment": " traing controls", - "training" : { - "systems": ["../data/"], - "set_prefix": "set", - "stop_batch": 1000000, - "batch_size": 1, - - "seed": 1, - - "_comment": " display and restart", - "_comment": " frequencies counted in batch", - "disp_file": "lcurve.out", - "disp_freq": 100, - "numb_test": 10, - "save_freq": 1000, - "save_ckpt": "model.ckpt", - "load_ckpt": "model.ckpt", - "disp_training":true, - "time_training":true, - "profiling": false, - "profiling_file": "timeline.json", - "_comment": "that's all" - }, - - "_comment": "that's all" -} - diff --git a/examples/water/train/water_srtab_example.json b/examples/water/train/water_srtab_example.json deleted file mode 100644 index f2a0a4a39c..0000000000 --- a/examples/water/train/water_srtab_example.json +++ /dev/null @@ -1,74 +0,0 @@ -{ - "with_distrib": false, - "_comment": " model parameters", - "model":{ - "type_map": ["O", "H"], - "use_srtab": "your_tab", - "smin_alpha": float_alpha, - "sw_rmin": float_rmin, - "sw_rmax": float_rmax, - "descriptor": { - "type": "loc_frame", - "sel_a": [16, 32], - "sel_r": [30, 60], - "rcut": 6.00, - "axis_rule": [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0], - "_comment": " default rule: []", - "_comment": " user defined rule: for each type provides two axes, ", - "_comment": " for each axis: (a_or_r, type, idx)", - "_comment": " if type < 0, exclude type -(type+1)", - "_comment": " for water (O:0, H:1) it can be", - "_comment": " [0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0]", - "_comment": " that's all" - }, - "fitting_net": { - "neuron": [240, 120, 60, 30, 10], - "resnet_dt": true, - "seed": 1, - "_comment": " that's all" - }, - "_comment": " that's all" - }, - - "learning_rate" :{ - "type": "exp", - "decay_steps": 5000, - "start_lr": 0.001, - "stop_lr": 3.51e-8, - "_comment": "that's all" - }, - - "loss" : { - "start_pref_e": 0.02, - "limit_pref_e": 8, - "start_pref_f": 1000, - "limit_pref_f": 1, - "start_pref_v": 0, - "limit_pref_v": 0, - "_comment": " that's all" - }, - - "_comment": " training controls", - "training" : { - "systems": ["../data/"], - "set_prefix": "set", - "stop_batch": 1000000, - "batch_size": 4, - - "seed": 1, - - "_comment": " display and restart", - "_comment": " frequencies counted in batch", - "disp_file": "lcurve.out", - "disp_freq": 100, - "numb_test": 10, - "save_freq": 1000, - "save_ckpt": "model.ckpt", - "load_ckpt": "model.ckpt", - "disp_training":true, - "time_training":true, - "_comment": "that's all" - }, - "_comment": "that's all" -} - diff --git a/setup.py b/setup.py index e77b1b78a7..22d9316f39 100644 --- a/setup.py +++ b/setup.py @@ -102,6 +102,8 @@ "-DBUILD_PY_IF:BOOL=TRUE", "-DBUILD_CPP_IF:BOOL=FALSE", "-DFLOAT_PREC:STRING=low", + "-DPADDLE_ROOT=$paddle_root", + "-DUSE_CUDA_TOOLKIT:BOOL=FALSE", ], cmake_source_dir="source", cmake_minimum_required_version="3.0", diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt old mode 100644 new mode 100755 index 908edd2b0a..457b370a48 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -5,7 +5,10 @@ set(CMAKE_LINK_WHAT_YOU_USE TRUE) # build cpp or python interfaces if (NOT DEFINED BUILD_CPP_IF) set(BUILD_CPP_IF TRUE) -endif (NOT DEFINED BUILD_CPP_IF) + message(WARNING "WARNING! -DON_INFER is set") + add_definitions("-DON_INFER") +endif() + if (NOT DEFINED BUILD_PY_IF) set(BUILD_PY_IF FALSE) endif (NOT DEFINED BUILD_PY_IF) @@ -44,7 +47,7 @@ endif(GIT_FOUND) # global defines list (APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/) -set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wno-ignored-attributes") +set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -Wno-ignored-attributes") # model version file(READ ${PROJECT_SOURCE_DIR}/config/MODEL_VER MODEL_VERSION) @@ -72,59 +75,64 @@ if (USE_CUDA_TOOLKIT) add_definitions("-D GOOGLE_CUDA") endif() -# find tensorflow, I need tf abi info +if(BUILD_PY_IF) find_package(tensorflow REQUIRED) +else() +find_package(Fluid REQUIRED) +endif() # find threads find_package(Threads) # auto op_cxx_abi +if (BUILD_PY_IF) if (NOT DEFINED OP_CXX_ABI) - if (BUILD_PY_IF) - if (DEFINED TENSORFLOW_ROOT) - set(FIND_ABI_CMD "import sys,os; sys.path.insert(0, os.path.join('${TENSORFLOW_ROOT}', '..')); import tensorflow; print(tensorflow.CXX11_ABI_FLAG if 'CXX11_ABI_FLAG' in tensorflow.__dict__ else tensorflow.sysconfig.CXX11_ABI_FLAG, end = '')" ) - else() - set(FIND_ABI_CMD "import tensorflow; print(tensorflow.CXX11_ABI_FLAG if 'CXX11_ABI_FLAG' in tensorflow.__dict__ else tensorflow.sysconfig.CXX11_ABI_FLAG, end = '')") - endif() - execute_process( - COMMAND ${PYTHON_EXECUTABLE} "-c" "${FIND_ABI_CMD}" - WORKING_DIRECTORY ${PROJECT_SOURCE_DIR} - OUTPUT_VARIABLE PY_CXX_ABI - RESULT_VARIABLE PY_CXX_ABI_RESULT_VAR - ERROR_VARIABLE PY_CXX_ABI_ERROR_VAR - ) - if (NOT ${PY_CXX_ABI_RESULT_VAR} EQUAL 0) - message(FATAL_ERROR "Cannot determine cxx abi, error message: ${PY_CXX_ABI_ERROR_VAR}") - endif() - set(OP_CXX_ABI ${PY_CXX_ABI}) - endif() - if (BUILD_CPP_IF) - try_run( - CPP_CXX_ABI_RUN_RESULT_VAR CPP_CXX_ABI_COMPILE_RESULT_VAR - ${CMAKE_CURRENT_BINARY_DIR}/tf_cxx_abi - "${CMAKE_CURRENT_SOURCE_DIR}/cmake/tf_cxx_abi.cpp" - LINK_LIBRARIES ${TensorFlowFramework_LIBRARY} - CMAKE_FLAGS "-DINCLUDE_DIRECTORIES:STRING=${TensorFlow_INCLUDE_DIRS}" - RUN_OUTPUT_VARIABLE CPP_CXX_ABI - COMPILE_OUTPUT_VARIABLE CPP_CXX_ABI_COMPILE_OUTPUT_VAR - ) - if (NOT ${CPP_CXX_ABI_COMPILE_RESULT_VAR}) - message(FATAL_ERROR "Failed to compile: \n ${CPP_CXX_ABI_COMPILE_OUTPUT_VAR}" ) - endif() - if (NOT ${CPP_CXX_ABI_RUN_RESULT_VAR} EQUAL "0") - message(FATAL_ERROR "Failed to run, return code: ${CPP_CXX_ABI}" ) - endif() - if (DEFINED PY_CXX_ABI) - if (NOT (${CPP_CXX_ABI} EQUAL ${PY_CXX_ABI})) + if (BUILD_PY_IF) + if (DEFINED TENSORFLOW_ROOT) + set(FIND_ABI_CMD "import sys,os; sys.path.insert(0, os.path.join('${TENSORFLOW_ROOT}', '..')); import tensorflow; print(tensorflow.CXX11_ABI_FLAG if 'CXX11_ABI_FLAG' in tensorflow.__dict__ else tensorflow.sysconfig.CXX11_ABI_FLAG, end = '')" ) + else() + set(FIND_ABI_CMD "import tensorflow; print(tensorflow.CXX11_ABI_FLAG if 'CXX11_ABI_FLAG' in tensorflow.__dict__ else tensorflow.sysconfig.CXX11_ABI_FLAG, end = '')") + endif() + execute_process( + COMMAND ${PYTHON_EXECUTABLE} "-c" "${FIND_ABI_CMD}" + WORKING_DIRECTORY ${PROJECT_SOURCE_DIR} + OUTPUT_VARIABLE PY_CXX_ABI + RESULT_VARIABLE PY_CXX_ABI_RESULT_VAR + ERROR_VARIABLE PY_CXX_ABI_ERROR_VAR + ) + if (NOT ${PY_CXX_ABI_RESULT_VAR} EQUAL 0) + message(FATAL_ERROR "Cannot determine cxx abi, error message: ${PY_CXX_ABI_ERROR_VAR}") + endif() + set(OP_CXX_ABI ${PY_CXX_ABI}) + endif() + if (BUILD_CPP_IF) + try_run( + CPP_CXX_ABI_RUN_RESULT_VAR CPP_CXX_ABI_COMPILE_RESULT_VAR + ${CMAKE_CURRENT_BINARY_DIR}/tf_cxx_abi + "${CMAKE_CURRENT_SOURCE_DIR}/cmake/tf_cxx_abi.cpp" + LINK_LIBRARIES ${TensorFlowFramework_LIBRARY} + CMAKE_FLAGS "-DINCLUDE_DIRECTORIES:STRING=${TensorFlow_INCLUDE_DIRS}" + RUN_OUTPUT_VARIABLE CPP_CXX_ABI + COMPILE_OUTPUT_VARIABLE CPP_CXX_ABI_COMPILE_OUTPUT_VAR + ) + if (NOT ${CPP_CXX_ABI_COMPILE_RESULT_VAR}) + message(FATAL_ERROR "Failed to compile: \n ${CPP_CXX_ABI_COMPILE_OUTPUT_VAR}" ) + endif() + if (NOT ${CPP_CXX_ABI_RUN_RESULT_VAR} EQUAL "0") + message(FATAL_ERROR "Failed to run, return code: ${CPP_CXX_ABI}" ) + endif() + if (DEFINED PY_CXX_ABI) + if (NOT (${CPP_CXX_ABI} EQUAL ${PY_CXX_ABI})) message (WARNNING "NOT consistent CXX_ABIs: python interface of tf uses ${PY_CXX_ABI}, while c++ interface of tf uses ${CPP_CXX_ABI}, we follow c++ interface ") - endif() - endif() - set(OP_CXX_ABI ${CPP_CXX_ABI}) - endif() - message (STATUS "Automatically determined OP_CXX_ABI=${OP_CXX_ABI} ") + endif() + endif() + set(OP_CXX_ABI ${CPP_CXX_ABI}) + endif() + message (STATUS "Automatically determined OP_CXX_ABI=${OP_CXX_ABI} ") else() - message (STATUS "User set OP_CXX_ABI=${OP_CXX_ABI} ") + message (STATUS "User set OP_CXX_ABI=${OP_CXX_ABI} ") endif() +message (STATUS "No set OP_CXX_ABI=${OP_CXX_ABI} ") # message the cxx_abi used during compiling if (${OP_CXX_ABI} EQUAL 0) message (STATUS "Set GLIBCXX_USE_CXX_ABI=0 when compiling ops") @@ -132,6 +140,7 @@ else () set (OP_CXX_ABI 1) message (STATUS "Set GLIBCXX_USE_CXX_ABI=1 when compiling ops") endif () +endif() # define USE_TTM if (NOT DEFINED USE_TTM) @@ -162,13 +171,14 @@ if (DEFINED FLOAT_PREC) string ( TOLOWER ${FLOAT_PREC} lower_float_prec ) if (lower_float_prec STREQUAL "high") set(PREC_DEF "-DHIGH_PREC") + add_definitions(${PREC_DEF}) else () set(PREC_DEF "") endif () else () set(PREC_DEF "-DHIGH_PREC") + add_definitions(${PREC_DEF}) endif() -add_definitions (${PREC_DEF}) # find openmp find_package(OpenMP) @@ -207,9 +217,10 @@ if (BUILD_CPP_IF) endif () endif (BUILD_CPP_IF) -add_subdirectory (op/) +# add_subdirectory (op/) add_subdirectory (lib/) if (BUILD_PY_IF) + add_subdirectory (op/) add_subdirectory (config/) # add_subdirectory (tests/) endif (BUILD_PY_IF) @@ -218,7 +229,7 @@ if (BUILD_CPP_IF) add_subdirectory (lmp/) if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9) # add_subdirectory (md/) - add_subdirectory (ipi/) +# add_subdirectory (ipi/) endif () endif (BUILD_CPP_IF) diff --git a/source/api_cc/CMakeLists.txt b/source/api_cc/CMakeLists.txt old mode 100644 new mode 100755 index 855732b26b..b5872350f1 --- a/source/api_cc/CMakeLists.txt +++ b/source/api_cc/CMakeLists.txt @@ -21,10 +21,23 @@ if (USE_CUDA_TOOLKIT) target_link_libraries (${libname} ${CUDA_LIBRARIES}) endif() +if(PADDLE_FOUND) + target_link_libraries(${libname} + ${PADDLE_LIBRARIES} + ${PADDLE_THIRD_PARTY_LIBRARIES} + rt dl pthread) + if (mklml_FOUND) + target_link_libraries(${libname} "-L${THIRD_PARTY_ROOT}/install/mklml/lib -liomp5 -Wl,--as-needed") + endif() + +else() + message(FATAL_ERROR "Cannot find PaddlePaddle Fluid under ${PADDLE_ROOT}") +endif() + + install(TARGETS ${libname} DESTINATION lib/) install( FILES ${INC_SRC} DESTINATION include/deepmd -) - +) \ No newline at end of file diff --git a/source/api_cc/include/AtomMap.h b/source/api_cc/include/AtomMap.h old mode 100644 new mode 100755 diff --git a/source/api_cc/include/DataModifier.h b/source/api_cc/include/DataModifier.h deleted file mode 100644 index 114abfd889..0000000000 --- a/source/api_cc/include/DataModifier.h +++ /dev/null @@ -1,52 +0,0 @@ -#pragma once - -#include "DeepPot.h" - -namespace deepmd{ -class DipoleChargeModifier -{ -public: - DipoleChargeModifier(); - DipoleChargeModifier(const std::string & model, - const int & gpu_rank = 0, - const std::string & name_scope = ""); - ~DipoleChargeModifier () {}; - void init (const std::string & model, - const int & gpu_rank = 0, - const std::string & name_scope = ""); - void print_summary(const std::string &pre) const; -public: - void compute (std::vector & dfcorr_, - std::vector & dvcorr_, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const std::vector> & pairs, - const std::vector & delef_, - const int nghost, - const InputNlist & lmp_list); - VALUETYPE cutoff () const {assert(inited); return rcut;}; - int numb_types () const {assert(inited); return ntypes;}; - std::vector sel_types () const {assert(inited); return sel_type;}; -private: - tensorflow::Session* session; - std::string name_scope, name_prefix; - int num_intra_nthreads, num_inter_nthreads; - tensorflow::GraphDef graph_def; - bool inited; - VALUETYPE rcut; - VALUETYPE cell_size; - int ntypes; - std::string model_type; - std::vector sel_type; - template VT get_scalar(const std::string & name) const; - template void get_vector(std::vector & vec, const std::string & name) const; - void run_model (std::vector & dforce, - std::vector & dvirial, - tensorflow::Session * session, - const std::vector> & input_tensors, - const AtomMap & atommap, - const int nghost); -}; -} - diff --git a/source/api_cc/include/DeepTensor.h b/source/api_cc/include/DeepTensor.h deleted file mode 100644 index bfa5cd1ba8..0000000000 --- a/source/api_cc/include/DeepTensor.h +++ /dev/null @@ -1,65 +0,0 @@ -#pragma once - -#include "common.h" -#include "neighbor_list.h" - -namespace deepmd{ -class DeepTensor -{ -public: - DeepTensor(); - DeepTensor(const std::string & model, - const int & gpu_rank = 0, - const std::string &name_scope = ""); - void init (const std::string & model, - const int & gpu_rank = 0, - const std::string &name_scope = ""); - void print_summary(const std::string &pre) const; -public: - void compute (std::vector & value, - const std::vector & coord, - const std::vector & atype, - const std::vector & box); - void compute (std::vector & value, - const std::vector & coord, - const std::vector & atype, - const std::vector & box, - const int nghost, - const InputNlist & inlist); - VALUETYPE cutoff () const {assert(inited); return rcut;}; - int numb_types () const {assert(inited); return ntypes;}; - int output_dim () const {assert(inited); return odim;}; - const std::vector & sel_types () const {assert(inited); return sel_type;}; -private: - tensorflow::Session* session; - std::string name_scope; - int num_intra_nthreads, num_inter_nthreads; - tensorflow::GraphDef graph_def; - bool inited; - VALUETYPE rcut; - VALUETYPE cell_size; - int ntypes; - std::string model_type; - std::string model_version; - int odim; - std::vector sel_type; - template VT get_scalar(const std::string & name) const; - template void get_vector (std::vector & vec, const std::string & name) const; - void run_model (std::vector & d_tensor_, - tensorflow::Session * session, - const std::vector> & input_tensors, - const AtomMap & atommap, - const int nghost = 0); - void compute_inner (std::vector & value, - const std::vector & coord, - const std::vector & atype, - const std::vector & box); - void compute_inner (std::vector & value, - const std::vector & coord, - const std::vector & atype, - const std::vector & box, - const int nghost, - const InputNlist& inlist); -}; -} - diff --git a/source/api_cc/include/DeepPot.h b/source/api_cc/include/PaddleDeepPot.h old mode 100644 new mode 100755 similarity index 79% rename from source/api_cc/include/DeepPot.h rename to source/api_cc/include/PaddleDeepPot.h index 63cf568376..d601bb865e --- a/source/api_cc/include/DeepPot.h +++ b/source/api_cc/include/PaddleDeepPot.h @@ -1,16 +1,16 @@ #pragma once - -#include "common.h" +#include "paddle/include/paddle_inference_api.h" +#include "paddle_common.h" #include "neighbor_list.h" namespace deepmd{ -class DeepPot +class PaddleDeepPot { public: - DeepPot () ; - ~DeepPot() ; - DeepPot (const std::string & model, const int & gpu_rank = 0, const std::string & file_content = ""); - void init (const std::string & model, const int & gpu_rank = 0, const std::string & file_content = ""); + PaddleDeepPot() ; + ~PaddleDeepPot() ; + PaddleDeepPot(std::string & prog_str, std::string & params_str); + void init (std::string & prog_str, std::string & params_str); void print_summary(const std::string &pre) const; public: void compute (ENERGYTYPE & ener, @@ -60,24 +60,24 @@ class DeepPot int dim_fparam () const {assert(inited); return dfparam;}; int dim_aparam () const {assert(inited); return daparam;}; void get_type_map (std::string & type_map); -private: - tensorflow::Session* session; - int num_intra_nthreads, num_inter_nthreads; - tensorflow::GraphDef graph_def; + void validate_fparam_aparam(const int & nloc, + const std::vector &fparam, + const std::vector &aparam, + const int &dfparam, + const int &daparam)const ; +public: + paddle_infer::Config config; + std::shared_ptr predictor; + int math_lib_num_threads; bool inited; template VT get_scalar(const std::string & name) const; - // VALUETYPE get_rcut () const; - // int get_ntypes () const; - VALUETYPE rcut; + + VALUETYPE rcut = 6.0; VALUETYPE cell_size; - std::string model_type; - std::string model_version; - int ntypes; + int ntypes = 2; int dfparam; int daparam; - void validate_fparam_aparam(const int & nloc, - const std::vector &fparam, - const std::vector &aparam)const ; + void compute_inner (ENERGYTYPE & ener, std::vector & force, std::vector & virial, @@ -89,24 +89,22 @@ class DeepPot const std::vector& fparam = std::vector(), const std::vector& aparam = std::vector()); - // copy neighbor list info from host bool init_nbor; std::vector sec_a; NeighborListData nlist_data; InputNlist nlist; AtomMap atommap; - // function used for neighbor list copy std::vector get_sel_a() const; }; -class DeepPotModelDevi +class PaddleDeepPotModelDevi { public: - DeepPotModelDevi () ; - ~DeepPotModelDevi() ; - DeepPotModelDevi (const std::vector & models, const int & gpu_rank = 0, const std::vector & file_contents = std::vector()); - void init (const std::vector & models, const int & gpu_rank = 0, const std::vector & file_contents = std::vector()); + PaddleDeepPotModelDevi () ; + ~PaddleDeepPotModelDevi() ; + PaddleDeepPotModelDevi (std::vector & prog_strs, std::vector & params_strs); + void init (std::vector& prog_strs, std::vector& params_strs); public: void compute (std::vector & all_ener, std::vector > & all_force, @@ -153,15 +151,18 @@ class DeepPotModelDevi void compute_relative_std_f (std::vector & std, const std::vector & avg, const VALUETYPE eps); -private: + void validate_fparam_aparam(const int & nloc, + const std::vector &fparam, + const std::vector &aparam, + const int &dfparam, + const int &daparam)const ; +public: unsigned numb_models; - std::vector sessions; - int num_intra_nthreads, num_inter_nthreads; - std::vector graph_defs; + std::vector configs; + int math_lib_num_threads; + std::vector> predictors; bool inited; template VT get_scalar(const std::string name) const; - // VALUETYPE get_rcut () const; - // int get_ntypes () const; VALUETYPE rcut; VALUETYPE cell_size; std::string model_type; @@ -169,21 +170,15 @@ class DeepPotModelDevi int ntypes; int dfparam; int daparam; - void validate_fparam_aparam(const int & nloc, - const std::vector &fparam, - const std::vector &aparam)const ; + - // copy neighbor list info from host bool init_nbor; std::vector > sec; deepmd::AtomMap atommap; NeighborListData nlist_data; InputNlist nlist; - // function used for nborlist copy std::vector > get_sel() const; - void cum_sum(const std::vector > n_sel); + void cum_sum(const std::vector > n_sel); }; } - - diff --git a/source/api_cc/include/common.h b/source/api_cc/include/common.h old mode 100644 new mode 100755 index c489053b6c..ace389431f --- a/source/api_cc/include/common.h +++ b/source/api_cc/include/common.h @@ -6,13 +6,8 @@ #include "version.h" #include "neighbor_list.h" #include "AtomMap.h" - -#include "tensorflow/core/platform/env.h" -#include "tensorflow/core/public/session.h" -#include "tensorflow/core/public/version.h" -#include -#include - +#include "string.h" +#include namespace deepmd{ @@ -25,9 +20,12 @@ typedef std::string STRINGTYPE; #ifdef HIGH_PREC typedef double VALUETYPE; typedef double ENERGYTYPE; +// typedef float VALUETYPE; +// typedef float ENERGYTYPE; #else typedef float VALUETYPE; typedef double ENERGYTYPE; +// typedef float ENERGYTYPE; #endif struct NeighborListData @@ -77,53 +75,9 @@ void get_env_nthreads(int & num_intra_nthreads, int & num_inter_nthreads); -void -check_status( - const tensorflow::Status& status); - std::string name_prefix( const std::string & name_scope); -template -VT -session_get_scalar( - tensorflow::Session* session, - const std::string name, - const std::string scope = ""); - -template -void -session_get_vector( - std::vector & o_vec, - tensorflow::Session* session, - const std::string name_, - const std::string scope = ""); - -int -session_input_tensors (std::vector> & input_tensors, - const std::vector & dcoord_, - const int & ntypes, - const std::vector & datype_, - const std::vector & dbox, - const VALUETYPE & cell_size, - const std::vector & fparam_, - const std::vector & aparam_, - const deepmd::AtomMap&atommap, - const std::string scope = ""); - -int -session_input_tensors (std::vector> & input_tensors, - const std::vector & dcoord_, - const int & ntypes, - const std::vector & datype_, - const std::vector & dbox, - InputNlist & dlist, - const std::vector & fparam_, - const std::vector & aparam_, - const deepmd::AtomMap&atommap, - const int nghost, - const int ago, - const std::string scope = ""); } diff --git a/source/api_cc/include/custom_op.h b/source/api_cc/include/custom_op.h old mode 100644 new mode 100755 diff --git a/source/api_cc/include/paddle_common.h b/source/api_cc/include/paddle_common.h new file mode 100755 index 0000000000..f81737d636 --- /dev/null +++ b/source/api_cc/include/paddle_common.h @@ -0,0 +1,37 @@ +#pragma once +#include +#include +#include +#include "neighbor_list.h" +#include "AtomMap.h" +#include "common.h" +#include "paddle/include/paddle_inference_api.h" + +using namespace std; + +namespace deepmd{ + int get_math_lib_num_threads(); + + int paddle_input_tensors ( + std::shared_ptr predictor_, + const std::vector & dcoord_, + const int & ntypes, + const std::vector & datype_, + const std::vector & dbox, + const deepmd::VALUETYPE & cell_size, + const std::vector & fparam_, + const std::vector & aparam_, + const deepmd::AtomMap& atommap); + + int paddle_input_tensors (std::shared_ptr predictor_, + const std::vector & dcoord_, + const int & ntypes, + const std::vector & datype_, + const std::vector & dbox, + InputNlist & dlist, + const std::vector & fparam_, + const std::vector & aparam_, + const deepmd::AtomMap&atommap, + const int nghost, + int ago=0); +} diff --git a/source/api_cc/include/version.h.in b/source/api_cc/include/version.h.in old mode 100644 new mode 100755 diff --git a/source/api_cc/src/AtomMap.cc b/source/api_cc/src/AtomMap.cc old mode 100644 new mode 100755 diff --git a/source/api_cc/src/DataModifier.cc b/source/api_cc/src/DataModifier.cc deleted file mode 100644 index f704ba599e..0000000000 --- a/source/api_cc/src/DataModifier.cc +++ /dev/null @@ -1,252 +0,0 @@ -#include "DataModifier.h" - -using namespace deepmd; -using namespace tensorflow; - -DipoleChargeModifier:: -DipoleChargeModifier() - : inited (false) -{ -} - -DipoleChargeModifier:: -DipoleChargeModifier(const std::string & model, - const int & gpu_rank, - const std::string &name_scope_) - : inited (false), name_scope(name_scope_) -{ - init(model, gpu_rank); -} - -void -DipoleChargeModifier:: -init (const std::string & model, - const int & gpu_rank, - const std::string &name_scope_) -{ - if (inited){ - std::cerr << "WARNING: deepmd-kit should not be initialized twice, do nothing at the second call of initializer" << std::endl; - return ; - } - name_scope = name_scope_; - SessionOptions options; - get_env_nthreads(num_intra_nthreads, num_inter_nthreads); - options.config.set_inter_op_parallelism_threads(num_inter_nthreads); - options.config.set_intra_op_parallelism_threads(num_intra_nthreads); - deepmd::check_status(NewSession(options, &session)); - deepmd::check_status(ReadBinaryProto(Env::Default(), model, &graph_def)); - deepmd::check_status(session->Create(graph_def)); - // int nnodes = graph_def.node_size(); - // for (int ii = 0; ii < nnodes; ++ii){ - // cout << ii << " \t " << graph_def.node(ii).name() << endl; - // } - rcut = get_scalar("descrpt_attr/rcut"); - cell_size = rcut; - ntypes = get_scalar("descrpt_attr/ntypes"); - model_type = get_scalar("model_attr/model_type"); - get_vector(sel_type, "model_attr/sel_type"); - sort(sel_type.begin(), sel_type.end()); - inited = true; -} - -template -VT -DipoleChargeModifier:: -get_scalar (const std::string & name) const -{ - return session_get_scalar(session, name, name_scope); -} - -template -void -DipoleChargeModifier:: -get_vector (std::vector & vec, const std::string & name) const -{ - session_get_vector(vec, session, name, name_scope); -} - -void -DipoleChargeModifier:: -run_model (std::vector & dforce, - std::vector & dvirial, - Session * session, - const std::vector> & input_tensors, - const AtomMap & atommap, - const int nghost) -{ - unsigned nloc = atommap.get_type().size(); - unsigned nall = nloc + nghost; - if (nloc == 0) { - dforce.clear(); - dvirial.clear(); - return; - } - - std::vector output_tensors; - deepmd::check_status (session->Run(input_tensors, - {"o_dm_force", "o_dm_virial", "o_dm_av"}, - {}, - &output_tensors)); - int cc = 0; - Tensor output_f = output_tensors[cc++]; - Tensor output_v = output_tensors[cc++]; - Tensor output_av = output_tensors[cc++]; - assert (output_f.dims() == 2), "dim of output tensor should be 2"; - assert (output_v.dims() == 2), "dim of output tensor should be 2"; - assert (output_av.dims() == 2), "dim of output tensor should be 2"; - int nframes = output_f.dim_size(0); - int natoms = output_f.dim_size(1) / 3; - assert (output_f.dim_size(0) == 1), "nframes should match"; - assert (natoms == nall), "natoms should be nall"; - assert (output_v.dim_size(0) == nframes), "nframes should match"; - assert (output_v.dim_size(1) == 9), "dof of virial should be 9"; - assert (output_av.dim_size(0) == nframes), "nframes should match"; - assert (output_av.dim_size(1) == natoms * 9), "dof of atom virial should be 9 * natoms"; - - auto of = output_f.flat (); - auto ov = output_v.flat (); - - dforce.resize(nall*3); - dvirial.resize(9); - for (int ii = 0; ii < nall * 3; ++ii){ - dforce[ii] = of(ii); - } - for (int ii = 0; ii < 9; ++ii){ - dvirial[ii] = ov(ii); - } -} - - - -void -DipoleChargeModifier:: -compute (std::vector & dfcorr_, - std::vector & dvcorr_, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const std::vector>& pairs, - const std::vector & delef_, - const int nghost, - const InputNlist & lmp_list) -{ - // firstly do selection - int nall = datype_.size(); - int nloc = nall - nghost; - int nghost_real; - std::vector real_fwd_map, real_bkw_map; - select_real_atoms(real_fwd_map, real_bkw_map, nghost_real, dcoord_, datype_, nghost, ntypes); - int nall_real = real_bkw_map.size(); - int nloc_real = nall_real - nghost_real; - if (nloc_real == 0){ - dfcorr_.resize(nall * 3); - dvcorr_.resize(9); - fill(dfcorr_.begin(), dfcorr_.end(), 0.0); - fill(dvcorr_.begin(), dvcorr_.end(), 0.0); - return; - } - // resize to nall_real - std::vector dcoord_real; - std::vector delef_real; - std::vector datype_real; - dcoord_real.resize(nall_real * 3); - delef_real.resize(nall_real * 3); - datype_real.resize(nall_real); - // fwd map - select_map(dcoord_real, dcoord_, real_fwd_map, 3); - select_map(delef_real, delef_, real_fwd_map, 3); - select_map(datype_real, datype_, real_fwd_map, 1); - // internal nlist - NeighborListData nlist_data; - nlist_data.copy_from_nlist(lmp_list); - nlist_data.shuffle_exclude_empty(real_fwd_map); - // sort atoms - AtomMap atommap (datype_real.begin(), datype_real.begin() + nloc_real); - assert (nloc_real == atommap.get_type().size()); - const std::vector & sort_fwd_map(atommap.get_fwd_map()); - const std::vector & sort_bkw_map(atommap.get_bkw_map()); - // shuffle nlist - nlist_data.shuffle(atommap); - InputNlist nlist; - nlist_data.make_inlist(nlist); - // make input tensors - std::vector> input_tensors; - int ret = session_input_tensors (input_tensors, dcoord_real, ntypes, datype_real, dbox, nlist, std::vector(), std::vector(), atommap, nghost_real, 0, name_scope); - assert (nloc_real == ret); - // make bond idx map - std::vector bd_idx(nall, -1); - for (int ii = 0; ii < pairs.size(); ++ii){ - bd_idx[pairs[ii].first] = pairs[ii].second; - } - // make extf by bond idx map - std::vector dtype_sort_loc = atommap.get_type(); - std::vector dextf; - for(int ii = 0; ii < dtype_sort_loc.size(); ++ii){ - if (binary_search(sel_type.begin(), sel_type.end(), dtype_sort_loc[ii])){ - // selected atom - int first_idx = real_bkw_map[sort_bkw_map[ii]]; - int second_idx = bd_idx[first_idx]; - assert(second_idx >= 0); - dextf.push_back(delef_[second_idx*3+0]); - dextf.push_back(delef_[second_idx*3+1]); - dextf.push_back(delef_[second_idx*3+2]); - } - } - // dextf should be loc and virtual - assert(dextf.size() == (nloc - nloc_real)*3); - // make tensor for extf - int nframes = 1; - TensorShape extf_shape ; - extf_shape.AddDim (nframes); - extf_shape.AddDim (dextf.size()); -#ifdef HIGH_PREC - Tensor extf_tensor (DT_DOUBLE, extf_shape); -#else - Tensor extf_tensor (DT_FLOAT, extf_shape); -#endif - auto extf = extf_tensor.matrix (); - for (int ii = 0; ii < nframes; ++ii){ - for (int jj = 0; jj < extf.size(); ++jj){ - extf(ii,jj) = dextf[jj]; - } - } - // append extf to input tensor - input_tensors.push_back({"t_ef", extf_tensor}); - // run model - std::vector dfcorr, dvcorr; - run_model (dfcorr, dvcorr, session, input_tensors, atommap, nghost_real); - assert(dfcorr.size() == nall_real * 3); - // back map force - std::vector dfcorr_1 = dfcorr; - atommap.backward (dfcorr_1.begin(), dfcorr.begin(), 3); - assert(dfcorr_1.size() == nall_real * 3); - // resize to all and clear - std::vector dfcorr_2(nall*3); - fill(dfcorr_2.begin(), dfcorr_2.end(), 0.0); - // back map to original position - for (int ii = 0; ii < nall_real; ++ii){ - for (int dd = 0; dd < 3; ++dd){ - dfcorr_2[real_bkw_map[ii]*3+dd] += dfcorr_1[ii*3+dd]; - } - } - // self correction of bonded force - for (int ii = 0; ii < pairs.size(); ++ii){ - for (int dd = 0; dd < 3; ++dd){ - dfcorr_2[pairs[ii].first*3+dd] += delef_[pairs[ii].second*3+dd]; - } - } - // add ele contrinution - dfcorr_ = dfcorr_2; - // for (int ii = 0; ii < nloc; ++ii){ - // for (int dd = 0; dd < 3; ++dd){ - // dfcorr_[ii*3+dd] += delef_[ii*3+dd]; - // } - // } - for (int ii = 0; ii < nloc_real; ++ii){ - int oii = real_bkw_map[ii]; - for (int dd = 0; dd < 3; ++dd){ - dfcorr_[oii*3+dd] += delef_[oii*3+dd]; - } - } - dvcorr_ = dvcorr; -} diff --git a/source/api_cc/src/DeepPot.cc b/source/api_cc/src/DeepPot.cc deleted file mode 100644 index b9412a505b..0000000000 --- a/source/api_cc/src/DeepPot.cc +++ /dev/null @@ -1,939 +0,0 @@ -#include "DeepPot.h" -#include "AtomMap.h" -#include - -using namespace tensorflow; -using namespace deepmd; - -#if GOOGLE_CUDA -#include "cuda_runtime.h" - -#define cudaErrcheck(res) { cudaAssert((res), __FILE__, __LINE__); } -inline void cudaAssert(cudaError_t code, const char *file, int line, bool abort=true) -{ - if (code != cudaSuccess) - { - fprintf(stderr,"cuda assert: %s %s %d\n", cudaGetErrorString(code), file, line); - if (abort) exit(code); - } -} -#endif - - -static -std::vector cum_sum (const std::vector & n_sel) { - std::vector sec; - sec.resize (n_sel.size() + 1); - sec[0] = 0; - for (int ii = 1; ii < sec.size(); ++ii) { - sec[ii] = sec[ii-1] + n_sel[ii-1]; - } - return sec; -} - - -static void -run_model (ENERGYTYPE & dener, - std::vector & dforce_, - std::vector & dvirial, - Session * session, - const std::vector> & input_tensors, - const AtomMap& atommap, - const int nghost = 0) -{ - unsigned nloc = atommap.get_type().size(); - unsigned nall = nloc + nghost; - if (nloc == 0) { - dener = 0; - // no backward map needed - // dforce of size nall * 3 - dforce_.resize(nall * 3); - fill(dforce_.begin(), dforce_.end(), 0.0); - // dvirial of size 9 - dvirial.resize(9); - fill(dvirial.begin(), dvirial.end(), 0.0); - return; - } - - std::vector output_tensors; - check_status (session->Run(input_tensors, - {"o_energy", "o_force", "o_atom_virial"}, - {}, - &output_tensors)); - - Tensor output_e = output_tensors[0]; - Tensor output_f = output_tensors[1]; - Tensor output_av = output_tensors[2]; - - auto oe = output_e.flat (); - auto of = output_f.flat (); - auto oav = output_av.flat (); - - dener = oe(0); - std::vector dforce (3 * nall); - dvirial.resize (9); - for (unsigned ii = 0; ii < nall * 3; ++ii){ - dforce[ii] = of(ii); - } - for (int ii = 0; ii < nall; ++ii) { - dvirial[0] += 1.0 * oav(9*ii+0); - dvirial[1] += 1.0 * oav(9*ii+1); - dvirial[2] += 1.0 * oav(9*ii+2); - dvirial[3] += 1.0 * oav(9*ii+3); - dvirial[4] += 1.0 * oav(9*ii+4); - dvirial[5] += 1.0 * oav(9*ii+5); - dvirial[6] += 1.0 * oav(9*ii+6); - dvirial[7] += 1.0 * oav(9*ii+7); - dvirial[8] += 1.0 * oav(9*ii+8); - } - dforce_ = dforce; - atommap.backward (dforce_.begin(), dforce.begin(), 3); -} - -static void run_model (ENERGYTYPE & dener, - std::vector& dforce_, - std::vector& dvirial, - std::vector& datom_energy_, - std::vector& datom_virial_, - Session* session, - const std::vector> & input_tensors, - const deepmd::AtomMap & atommap, - const int& nghost = 0) -{ - unsigned nloc = atommap.get_type().size(); - unsigned nall = nloc + nghost; - if (nloc == 0) { - dener = 0; - // no backward map needed - // dforce of size nall * 3 - dforce_.resize(nall * 3); - fill(dforce_.begin(), dforce_.end(), 0.0); - // dvirial of size 9 - dvirial.resize(9); - fill(dvirial.begin(), dvirial.end(), 0.0); - // datom_energy_ of size nall - datom_energy_.resize(nall); - fill(datom_energy_.begin(), datom_energy_.end(), 0.0); - // datom_virial_ of size nall * 9 - datom_virial_.resize(nall * 9); - fill(datom_virial_.begin(), datom_virial_.end(), 0.0); - return; - } - std::vector output_tensors; - - check_status (session->Run(input_tensors, - {"o_energy", "o_force", "o_atom_energy", "o_atom_virial"}, - {}, - &output_tensors)); - - Tensor output_e = output_tensors[0]; - Tensor output_f = output_tensors[1]; - Tensor output_ae = output_tensors[2]; - Tensor output_av = output_tensors[3]; - - auto oe = output_e.flat (); - auto of = output_f.flat (); - auto oae = output_ae.flat (); - auto oav = output_av.flat (); - - dener = oe(0); - std::vector dforce (3 * nall); - std::vector datom_energy (nall, 0); - std::vector datom_virial (9 * nall); - dvirial.resize (9); - for (int ii = 0; ii < nall * 3; ++ii) { - dforce[ii] = of(ii); - } - for (int ii = 0; ii < nloc; ++ii) { - datom_energy[ii] = oae(ii); - } - for (int ii = 0; ii < nall * 9; ++ii) { - datom_virial[ii] = oav(ii); - } - for (int ii = 0; ii < nall; ++ii) { - dvirial[0] += 1.0 * datom_virial[9*ii+0]; - dvirial[1] += 1.0 * datom_virial[9*ii+1]; - dvirial[2] += 1.0 * datom_virial[9*ii+2]; - dvirial[3] += 1.0 * datom_virial[9*ii+3]; - dvirial[4] += 1.0 * datom_virial[9*ii+4]; - dvirial[5] += 1.0 * datom_virial[9*ii+5]; - dvirial[6] += 1.0 * datom_virial[9*ii+6]; - dvirial[7] += 1.0 * datom_virial[9*ii+7]; - dvirial[8] += 1.0 * datom_virial[9*ii+8]; - } - dforce_ = dforce; - datom_energy_ = datom_energy; - datom_virial_ = datom_virial; - atommap.backward (dforce_.begin(), dforce.begin(), 3); - atommap.backward (datom_energy_.begin(), datom_energy.begin(), 1); - atommap.backward (datom_virial_.begin(), datom_virial.begin(), 9); -} - - -DeepPot:: -DeepPot () - : inited (false), init_nbor (false) -{ - get_env_nthreads(num_intra_nthreads, num_inter_nthreads); -} - -DeepPot:: -DeepPot (const std::string & model, const int & gpu_rank, const std::string & file_content) - : inited (false), init_nbor (false) -{ - get_env_nthreads(num_intra_nthreads, num_inter_nthreads); - init(model, gpu_rank, file_content); -} - -DeepPot::~DeepPot() {} - -void -DeepPot:: -init (const std::string & model, const int & gpu_rank, const std::string & file_content) -{ - if (inited){ - std::cerr << "WARNING: deepmd-kit should not be initialized twice, do nothing at the second call of initializer" << std::endl; - return ; - } - SessionOptions options; - get_env_nthreads(num_intra_nthreads, num_inter_nthreads); - options.config.set_inter_op_parallelism_threads(num_inter_nthreads); - options.config.set_intra_op_parallelism_threads(num_intra_nthreads); - - if(file_content.size() == 0) - check_status (ReadBinaryProto(Env::Default(), model, &graph_def)); - else - graph_def.ParseFromString(file_content); - int gpu_num = -1; - #if GOOGLE_CUDA - cudaGetDeviceCount(&gpu_num); // check current device environment - if (gpu_num > 0) { - options.config.set_allow_soft_placement(true); - options.config.mutable_gpu_options()->set_per_process_gpu_memory_fraction(0.9); - options.config.mutable_gpu_options()->set_allow_growth(true); - cudaErrcheck(cudaSetDevice(gpu_rank % gpu_num)); - std::string str = "/gpu:"; - str += std::to_string(gpu_rank % gpu_num); - graph::SetDefaultDevice(str, &graph_def); - } - #endif // GOOGLE_CUDA - check_status (NewSession(options, &session)); - check_status (session->Create(graph_def)); - rcut = get_scalar("descrpt_attr/rcut"); - cell_size = rcut; - ntypes = get_scalar("descrpt_attr/ntypes"); - dfparam = get_scalar("fitting_attr/dfparam"); - daparam = get_scalar("fitting_attr/daparam"); - if (dfparam < 0) dfparam = 0; - if (daparam < 0) daparam = 0; - model_type = get_scalar("model_attr/model_type"); - model_version = get_scalar("model_attr/model_version"); - if(! model_compatable(model_version)){ - throw std::runtime_error( - "incompatable model: version " + model_version - + " in graph, but version " + global_model_version - + " supported "); - } - inited = true; - - init_nbor = false; -} - -void -DeepPot:: -print_summary(const std::string &pre) const -{ - std::cout << pre << "installed to: " + global_install_prefix << std::endl; - std::cout << pre << "source: " + global_git_summ << std::endl; - std::cout << pre << "source brach: " + global_git_branch << std::endl; - std::cout << pre << "source commit: " + global_git_hash << std::endl; - std::cout << pre << "source commit at: " + global_git_date << std::endl; - std::cout << pre << "surpport model ver.:" + global_model_version << std::endl; - std::cout << pre << "build float prec: " + global_float_prec << std::endl; - std::cout << pre << "build with tf inc: " + global_tf_include_dir << std::endl; - std::cout << pre << "build with tf lib: " + global_tf_lib << std::endl; - std::cout << pre << "set tf intra_op_parallelism_threads: " << num_intra_nthreads << std::endl; - std::cout << pre << "set tf inter_op_parallelism_threads: " << num_inter_nthreads << std::endl; -} - -template -VT -DeepPot:: -get_scalar (const std::string & name) const -{ - return session_get_scalar(session, name); -} - -std::string graph_info(const GraphDef & graph_def) { - // std::stringstream buffer; - // std::streambuf * old = std::cout.rdbuf(buffer.rdbuf()); - std::string str = ""; - for (int ii = 0; ii < graph_def.node_size(); ii++) { - if (graph_def.node(ii).name() == "DescrptSeA") { - // str = graph_def.node(ii).PrintDebugString(); - str = graph_def.node(ii).DebugString(); - return str; - // std::cout << str << std::endl; - } - if (graph_def.node(ii).name() == "DescrptSeR") { - // str = graph_def.node(ii).PrintDebugString(); - str = graph_def.node(ii).DebugString(); - return str; - // std::cout << str << std::endl; - } - } - return str; -} - -// init the tmp array data -std::vector DeepPot::get_sel_a () const { - std::vector sel_a; - std::istringstream is(graph_info(graph_def)); - std::string line = ""; - while(is >> line) { - if (line.find("sel_a") != line.npos) { - while (std::getline(is, line) && line != "}") { - if (line.find("i:") != line.npos) { - sel_a.push_back(atoi((line.substr(line.find("i:") + 2)).c_str())); - } - } break; - } - if (line.find("sel") != line.npos) { - while (std::getline(is, line) && line != "}") { - if (line.find("i:") != line.npos) { - sel_a.push_back(atoi((line.substr(line.find("i:") + 2)).c_str())); - } - } break; - } - } - return sel_a; -} - -void -DeepPot:: -validate_fparam_aparam(const int & nloc, - const std::vector &fparam, - const std::vector &aparam)const -{ - if (fparam.size() != dfparam) { - throw std::runtime_error("the dim of frame parameter provided is not consistent with what the model uses"); - } - if (aparam.size() != daparam * nloc) { - throw std::runtime_error("the dim of atom parameter provided is not consistent with what the model uses"); - } -} - -void -DeepPot:: -compute (ENERGYTYPE & dener, - std::vector & dforce_, - std::vector & dvirial, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const std::vector & fparam, - const std::vector & aparam) -{ - int nall = dcoord_.size() / 3; - int nloc = nall; - atommap = deepmd::AtomMap (datype_.begin(), datype_.begin() + nloc); - assert (nloc == atommap.get_type().size()); - validate_fparam_aparam(nloc, fparam, aparam); - - std::vector> input_tensors; - int ret = session_input_tensors (input_tensors, dcoord_, ntypes, datype_, dbox, cell_size, fparam, aparam, atommap); - assert (ret == nloc); - - run_model (dener, dforce_, dvirial, session, input_tensors, atommap); -} - -void -DeepPot:: -compute (ENERGYTYPE & dener, - std::vector & dforce_, - std::vector & dvirial, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const int nghost, - const InputNlist & lmp_list, - const int& ago, - const std::vector & fparam, - const std::vector & aparam_) -{ - std::vector dcoord, dforce, aparam; - std::vector datype, fwd_map, bkw_map; - int nghost_real; - select_real_atoms(fwd_map, bkw_map, nghost_real, dcoord_, datype_, nghost, ntypes); - // resize to nall_real - dcoord.resize(bkw_map.size() * 3); - datype.resize(bkw_map.size()); - // fwd map - select_map(dcoord, dcoord_, fwd_map, 3); - select_map(datype, datype_, fwd_map, 1); - // aparam - if (daparam > 0){ - aparam.resize(bkw_map.size()); - select_map(aparam, aparam_, fwd_map, daparam); - } - // internal nlist - if (ago == 0){ - nlist_data.copy_from_nlist(lmp_list); - nlist_data.shuffle_exclude_empty(fwd_map); - } - compute_inner(dener, dforce, dvirial, dcoord, datype, dbox, nghost_real, ago, fparam, aparam); - // bkw map - dforce_.resize(fwd_map.size() * 3); - select_map(dforce_, dforce, bkw_map, 3); -} - -void -DeepPot:: -compute_inner (ENERGYTYPE & dener, - std::vector & dforce_, - std::vector & dvirial, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const int nghost, - const int& ago, - const std::vector & fparam, - const std::vector & aparam) -{ - int nall = dcoord_.size() / 3; - int nloc = nall - nghost; - - validate_fparam_aparam(nloc, fparam, aparam); - std::vector> input_tensors; - - // agp == 0 means that the LAMMPS nbor list has been updated - if (ago == 0) { - atommap = deepmd::AtomMap (datype_.begin(), datype_.begin() + nloc); - assert (nloc == atommap.get_type().size()); - nlist_data.shuffle(atommap); - nlist_data.make_inlist(nlist); - } - int ret = session_input_tensors (input_tensors, dcoord_, ntypes, datype_, dbox, nlist, fparam, aparam, atommap, nghost, ago); - assert (nloc == ret); - run_model (dener, dforce_, dvirial, session, input_tensors, atommap, nghost); -} - - -void -DeepPot:: -compute (ENERGYTYPE & dener, - std::vector & dforce_, - std::vector & dvirial, - std::vector & datom_energy_, - std::vector & datom_virial_, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const std::vector & fparam, - const std::vector & aparam) -{ - atommap = deepmd::AtomMap (datype_.begin(), datype_.end()); - validate_fparam_aparam(atommap.get_type().size(), fparam, aparam); - - std::vector> input_tensors; - int nloc = session_input_tensors (input_tensors, dcoord_, ntypes, datype_, dbox, cell_size, fparam, aparam, atommap); - - run_model (dener, dforce_, dvirial, datom_energy_, datom_virial_, session, input_tensors, atommap); -} - - - -void -DeepPot:: -compute (ENERGYTYPE & dener, - std::vector & dforce_, - std::vector & dvirial, - std::vector & datom_energy_, - std::vector & datom_virial_, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const int nghost, - const InputNlist & lmp_list, - const int & ago, - const std::vector & fparam, - const std::vector & aparam) -{ - int nall = dcoord_.size() / 3; - int nloc = nall - nghost; - validate_fparam_aparam(nloc, fparam, aparam); - std::vector> input_tensors; - - if (ago == 0) { - atommap = AtomMap (datype_.begin(), datype_.begin() + nloc); - assert (nloc == atommap.get_type().size()); - - nlist_data.copy_from_nlist(lmp_list); - nlist_data.shuffle(atommap); - nlist_data.make_inlist(nlist); - } - - int ret = session_input_tensors (input_tensors, dcoord_, ntypes, datype_, dbox, nlist, fparam, aparam, atommap, nghost, ago); - assert (nloc == ret); - run_model (dener, dforce_, dvirial, datom_energy_, datom_virial_, session, input_tensors, atommap, nghost); -} - -void -DeepPot:: -get_type_map(std::string & type_map){ - type_map = get_scalar("model_attr/tmap"); -} - - - -DeepPotModelDevi:: -DeepPotModelDevi () - : inited (false), - init_nbor (false), - numb_models (0) -{ - get_env_nthreads(num_intra_nthreads, num_inter_nthreads); -} - -DeepPotModelDevi:: -DeepPotModelDevi (const std::vector & models, const int & gpu_rank, const std::vector & file_contents) - : inited (false), - init_nbor(false), - numb_models (0) -{ - get_env_nthreads(num_intra_nthreads, num_inter_nthreads); - init(models, gpu_rank, file_contents); -} - -DeepPotModelDevi::~DeepPotModelDevi() {} - -void -DeepPotModelDevi:: -init (const std::vector & models, const int & gpu_rank, const std::vector & file_contents) -{ - if (inited){ - std::cerr << "WARNING: deepmd-kit should not be initialized twice, do nothing at the second call of initializer" << std::endl; - return ; - } - numb_models = models.size(); - sessions.resize(numb_models); - graph_defs.resize(numb_models); - - int gpu_num = -1; - #if GOOGLE_CUDA - cudaGetDeviceCount(&gpu_num); - #endif // GOOGLE_CUDA - - SessionOptions options; - options.config.set_inter_op_parallelism_threads(num_inter_nthreads); - options.config.set_intra_op_parallelism_threads(num_intra_nthreads); - for (unsigned ii = 0; ii < numb_models; ++ii){ - if (file_contents.size() == 0) - check_status (ReadBinaryProto(Env::Default(), models[ii], &graph_defs[ii])); - else - graph_defs[ii].ParseFromString(file_contents[ii]); - } - #if GOOGLE_CUDA - if (gpu_num > 0) { - options.config.set_allow_soft_placement(true); - options.config.mutable_gpu_options()->set_per_process_gpu_memory_fraction(0.9); - options.config.mutable_gpu_options()->set_allow_growth(true); - cudaErrcheck(cudaSetDevice(gpu_rank % gpu_num)); - } - #endif // GOOGLE_CUDA - - for (unsigned ii = 0; ii < numb_models; ++ii) { - if (gpu_num > 0) { - std::string str = "/gpu:"; - str += std::to_string(gpu_rank % gpu_num); - graph::SetDefaultDevice(str, &graph_defs[ii]); - } - check_status (NewSession(options, &(sessions[ii]))); - check_status (sessions[ii]->Create(graph_defs[ii])); - } - rcut = get_scalar("descrpt_attr/rcut"); - cell_size = rcut; - ntypes = get_scalar("descrpt_attr/ntypes"); - dfparam = get_scalar("fitting_attr/dfparam"); - daparam = get_scalar("fitting_attr/daparam"); - if (dfparam < 0) dfparam = 0; - if (daparam < 0) daparam = 0; - model_type = get_scalar("model_attr/model_type"); - model_version = get_scalar("model_attr/model_version"); - if(! model_compatable(model_version)){ - throw std::runtime_error( - "incompatable model: version " + model_version - + " in graph, but version " + global_model_version - + " supported "); - } - // rcut = get_rcut(); - // cell_size = rcut; - // ntypes = get_ntypes(); - inited = true; - - init_nbor = false; -} - -template -VT -DeepPotModelDevi:: -get_scalar(const std::string name) const -{ - VT myrcut; - for (unsigned ii = 0; ii < numb_models; ++ii){ - VT ret = session_get_scalar(sessions[ii], name); - if (ii == 0){ - myrcut = ret; - } - else { - assert (myrcut == ret); - } - } - return myrcut; -} - -// init the tmp array data -std::vector > -DeepPotModelDevi:: -get_sel () const -{ - std::vector > sec; - for (int ii = 0; ii < numb_models; ii++) { - std::vector sel; - std::istringstream is(graph_info(graph_defs[ii])); - std::string line = ""; - while(is >> line) { - if (line.find("sel") != line.npos) { - while (std::getline(is, line) && line != "}") { - if (line.find("i:") != line.npos) { - sel.push_back(atoi((line.substr(line.find("i:") + 2)).c_str())); - } - } break; - } - if (line.find("sel_a") != line.npos) { - while (std::getline(is, line) && line != "}") { - if (line.find("i:") != line.npos) { - sel.push_back(atoi((line.substr(line.find("i:") + 2)).c_str())); - } - } break; - } - } - sec.push_back(sel); - } - return sec; -} - -void -DeepPotModelDevi:: -cum_sum (const std::vector > n_sel) -{ - for (int ii = 0; ii < numb_models; ++ii) { - std::vector _sec; - _sec.resize (n_sel[ii].size() + 1); - _sec[0] = 0; - for (int jj = 1; jj < _sec.size(); ++jj) { - _sec[jj] = _sec[jj-1] + n_sel[ii][jj-1]; - } - sec.push_back(_sec); - } -} - -void -DeepPotModelDevi:: -validate_fparam_aparam(const int & nloc, - const std::vector &fparam, - const std::vector &aparam)const -{ - if (fparam.size() != dfparam) { - throw std::runtime_error("the dim of frame parameter provided is not consistent with what the model uses"); - } - if (aparam.size() != daparam * nloc) { - throw std::runtime_error("the dim of atom parameter provided is not consistent with what the model uses"); - } -} - -// void -// DeepPotModelDevi:: -// compute (ENERGYTYPE & dener, -// std::vector & dforce_, -// std::vector & dvirial, -// std::vector & model_devi, -// const std::vector & dcoord_, -// const std::vector & datype_, -// const std::vector & dbox, -// const std::vector & fparam, -// const std::vector & aparam) -// { -// if (numb_models == 0) return; - -// atommap = AtomMap (datype_.begin(), datype_.end()); -// validate_fparam_aparam(atommap.get_type().size(), fparam, aparam); - -// std::vector> input_tensors; -// int nloc = session_input_tensors (input_tensors, dcoord_, ntypes, datype_, dbox, cell_size, fparam, aparam, atommap); - -// std::vector all_energy (numb_models); -// std::vector > all_force (numb_models); -// std::vector > all_virial (numb_models); - -// for (unsigned ii = 0; ii < numb_models; ++ii){ -// run_model (all_energy[ii], all_force[ii], all_virial[ii], sessions[ii], input_tensors, atommap); -// } - -// dener = 0; -// for (unsigned ii = 0; ii < numb_models; ++ii){ -// dener += all_energy[ii]; -// } -// dener /= VALUETYPE(numb_models); -// compute_avg (dvirial, all_virial); -// compute_avg (dforce_, all_force); - -// compute_std_f (model_devi, dforce_, all_force); - -// // for (unsigned ii = 0; ii < numb_models; ++ii){ -// // cout << all_force[ii][573] << " " << all_force[ii][574] << " " << all_force[ii][575] << endl; -// // } -// // cout << dforce_[573] << " " -// // << dforce_[574] << " " -// // << dforce_[575] << " " -// // << model_devi[191] << endl; -// } - -void -DeepPotModelDevi:: -compute (std::vector & all_energy, - std::vector> & all_force, - std::vector> & all_virial, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const int nghost, - const InputNlist & lmp_list, - const int & ago, - const std::vector & fparam, - const std::vector & aparam) -{ - if (numb_models == 0) return; - int nall = dcoord_.size() / 3; - int nloc = nall - nghost; - validate_fparam_aparam(nloc, fparam, aparam); - std::vector> input_tensors; - - // agp == 0 means that the LAMMPS nbor list has been updated - if (ago == 0) { - atommap = AtomMap (datype_.begin(), datype_.begin() + nloc); - assert (nloc == atommap.get_type().size()); - - nlist_data.copy_from_nlist(lmp_list); - nlist_data.shuffle(atommap); - nlist_data.make_inlist(nlist); - } - int ret = session_input_tensors (input_tensors, dcoord_, ntypes, datype_, dbox, nlist, fparam, aparam, atommap, nghost, ago); - - all_energy.resize (numb_models); - all_force.resize (numb_models); - all_virial.resize (numb_models); - assert (nloc == ret); - for (unsigned ii = 0; ii < numb_models; ++ii) { - run_model (all_energy[ii], all_force[ii], all_virial[ii], sessions[ii], input_tensors, atommap, nghost); - } -} - -void -DeepPotModelDevi:: -compute (std::vector & all_energy, - std::vector> & all_force, - std::vector> & all_virial, - std::vector> & all_atom_energy, - std::vector> & all_atom_virial, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const int nghost, - const InputNlist & lmp_list, - const int & ago, - const std::vector & fparam, - const std::vector & aparam) -{ - if (numb_models == 0) return; - int nall = dcoord_.size() / 3; - int nloc = nall - nghost; - validate_fparam_aparam(nloc, fparam, aparam); - std::vector> input_tensors; - - // agp == 0 means that the LAMMPS nbor list has been updated - if (ago == 0) { - atommap = AtomMap (datype_.begin(), datype_.begin() + nloc); - assert (nloc == atommap.get_type().size()); - - nlist_data.copy_from_nlist(lmp_list); - nlist_data.shuffle(atommap); - nlist_data.make_inlist(nlist); - } - int ret = session_input_tensors (input_tensors, dcoord_, ntypes, datype_, dbox, nlist, fparam, aparam, atommap, nghost, ago); - - all_energy.resize (numb_models); - all_force .resize (numb_models); - all_virial.resize (numb_models); - all_atom_energy.resize (numb_models); - all_atom_virial.resize (numb_models); - assert (nloc == ret); - for (unsigned ii = 0; ii < numb_models; ++ii) { - run_model (all_energy[ii], all_force[ii], all_virial[ii], all_atom_energy[ii], all_atom_virial[ii], sessions[ii], input_tensors, atommap, nghost); - } -} - -void -DeepPotModelDevi:: -compute_avg (VALUETYPE & dener, - const std::vector & all_energy) -{ - assert (all_energy.size() == numb_models); - if (numb_models == 0) return; - - dener = 0; - for (unsigned ii = 0; ii < numb_models; ++ii){ - dener += all_energy[ii]; - } - dener /= (VALUETYPE)(numb_models); -} - -#ifndef HIGH_PREC -void -DeepPotModelDevi:: -compute_avg (ENERGYTYPE & dener, - const std::vector& all_energy) -{ - assert (all_energy.size() == numb_models); - if (numb_models == 0) return; - - dener = 0; - for (unsigned ii = 0; ii < numb_models; ++ii){ - dener += all_energy[ii]; - } - dener /= (ENERGYTYPE)(numb_models); -} -#endif - -void -DeepPotModelDevi:: -compute_avg (std::vector & avg, - const std::vector > & xx) -{ - assert (xx.size() == numb_models); - if (numb_models == 0) return; - - avg.resize(xx[0].size()); - fill (avg.begin(), avg.end(), VALUETYPE(0.)); - - for (unsigned ii = 0; ii < numb_models; ++ii){ - for (unsigned jj = 0; jj < avg.size(); ++jj){ - avg[jj] += xx[ii][jj]; - } - } - - for (unsigned jj = 0; jj < avg.size(); ++jj){ - avg[jj] /= VALUETYPE(numb_models); - } -} - - -// void -// DeepPotModelDevi:: -// compute_std (VALUETYPE & std, -// const VALUETYPE & avg, -// const vector& xx) -// { -// std = 0; -// assert(xx.size() == numb_models); -// for (unsigned jj = 0; jj < xx.size(); ++jj){ -// std += (xx[jj] - avg) * (xx[jj] - avg); -// } -// std = sqrt(std / VALUETYPE(numb_models)); -// // std = sqrt(std / VALUETYPE(numb_models-)); -// } - -void -DeepPotModelDevi:: -compute_std_e (std::vector & std, - const std::vector & avg, - const std::vector >&xx) -{ - assert (xx.size() == numb_models); - if (numb_models == 0) return; - - unsigned ndof = avg.size(); - unsigned nloc = ndof; - assert (nloc == ndof); - - std.resize(nloc); - fill (std.begin(), std.end(), VALUETYPE(0.)); - - for (unsigned ii = 0; ii < numb_models; ++ii) { - for (unsigned jj = 0 ; jj < nloc; ++jj){ - const VALUETYPE * tmp_f = &(xx[ii][jj]); - const VALUETYPE * tmp_avg = &(avg[jj]); - VALUETYPE vdiff = xx[ii][jj] - avg[jj]; - std[jj] += vdiff * vdiff; - } - } - - for (unsigned jj = 0; jj < nloc; ++jj){ - std[jj] = sqrt(std[jj] / VALUETYPE(numb_models)); - // std[jj] = sqrt(std[jj] / VALUETYPE(numb_models-1)); - } -} - -void -DeepPotModelDevi:: -compute_std_f (std::vector & std, - const std::vector & avg, - const std::vector >&xx) -{ - assert (xx.size() == numb_models); - if (numb_models == 0) return; - - unsigned ndof = avg.size(); - unsigned nloc = ndof / 3; - assert (nloc * 3 == ndof); - - std.resize(nloc); - fill (std.begin(), std.end(), VALUETYPE(0.)); - - for (unsigned ii = 0; ii < numb_models; ++ii) { - for (unsigned jj = 0 ; jj < nloc; ++jj){ - const VALUETYPE * tmp_f = &(xx[ii][jj*3]); - const VALUETYPE * tmp_avg = &(avg[jj*3]); - VALUETYPE vdiff[3]; - vdiff[0] = tmp_f[0] - tmp_avg[0]; - vdiff[1] = tmp_f[1] - tmp_avg[1]; - vdiff[2] = tmp_f[2] - tmp_avg[2]; - std[jj] += deepmd::dot3(vdiff, vdiff); - } - } - - for (unsigned jj = 0; jj < nloc; ++jj){ - std[jj] = sqrt(std[jj] / VALUETYPE(numb_models)); - // std[jj] = sqrt(std[jj] / VALUETYPE(numb_models-1)); - } -} - -void -DeepPotModelDevi:: -compute_relative_std_f (std::vector &std, - const std::vector &avg, - const VALUETYPE eps) -{ - unsigned nloc = std.size(); - for (unsigned ii = 0; ii < nloc; ++ii){ - const VALUETYPE * tmp_avg = &(avg[ii*3]); - VALUETYPE vdiff[3]; - vdiff[0] = tmp_avg[0]; - vdiff[1] = tmp_avg[1]; - vdiff[2] = tmp_avg[2]; - VALUETYPE f_norm = sqrt(deepmd::dot3(vdiff, vdiff)); - // relative std = std/(abs(f)+eps) - std[ii] /= f_norm + eps; - } -} - diff --git a/source/api_cc/src/DeepTensor.cc b/source/api_cc/src/DeepTensor.cc deleted file mode 100644 index a907ae0e19..0000000000 --- a/source/api_cc/src/DeepTensor.cc +++ /dev/null @@ -1,201 +0,0 @@ -#include "DeepTensor.h" - -using namespace deepmd; -using namespace tensorflow; - -DeepTensor:: -DeepTensor() - : inited (false) -{ -} - -DeepTensor:: -DeepTensor(const std::string & model, - const int & gpu_rank, - const std::string &name_scope_) - : inited (false), name_scope(name_scope_) -{ - init(model, gpu_rank); -} - -void -DeepTensor:: -init (const std::string & model, - const int & gpu_rank, - const std::string &name_scope_) -{ - if (inited){ - std::cerr << "WARNING: deepmd-kit should not be initialized twice, do nothing at the second call of initializer" << std::endl; - return ; - } - name_scope = name_scope_; - SessionOptions options; - get_env_nthreads(num_intra_nthreads, num_inter_nthreads); - options.config.set_inter_op_parallelism_threads(num_inter_nthreads); - options.config.set_intra_op_parallelism_threads(num_intra_nthreads); - deepmd::check_status (NewSession(options, &session)); - deepmd::check_status (ReadBinaryProto(Env::Default(), model, &graph_def)); - deepmd::check_status (session->Create(graph_def)); - rcut = get_scalar("descrpt_attr/rcut"); - cell_size = rcut; - ntypes = get_scalar("descrpt_attr/ntypes"); - odim = get_scalar("model_attr/output_dim"); - get_vector(sel_type, "model_attr/sel_type"); - model_type = get_scalar("model_attr/model_type"); - model_version = get_scalar("model_attr/model_version"); - if(! model_compatable(model_version)){ - throw std::runtime_error( - "incompatable model: version " + model_version - + " in graph, but version " + global_model_version - + " supported "); - } - inited = true; -} - -template -VT -DeepTensor:: -get_scalar (const std::string & name) const -{ - return session_get_scalar(session, name, name_scope); -} - -template -void -DeepTensor:: -get_vector (std::vector & vec, const std::string & name) const -{ - session_get_vector(vec, session, name, name_scope); -} - -void -DeepTensor:: -run_model (std::vector & d_tensor_, - Session * session, - const std::vector> & input_tensors, - const AtomMap &atommap, - const int nghost) -{ - unsigned nloc = atommap.get_type().size(); - unsigned nall = nloc + nghost; - if (nloc == 0) { - // return empty - d_tensor_.clear(); - return; - } - - std::vector output_tensors; - deepmd::check_status (session->Run(input_tensors, - {name_prefix(name_scope) + "o_" + model_type}, - {}, - &output_tensors)); - - Tensor output_t = output_tensors[0]; - assert (output_t.dims() == 1), "dim of output tensor should be 1"; - int o_size = output_t.dim_size(0); - - auto ot = output_t.flat (); - - std::vector d_tensor (o_size); - for (unsigned ii = 0; ii < o_size; ++ii){ - d_tensor[ii] = ot(ii); - } - d_tensor_ = d_tensor; -} - - -void -DeepTensor:: -compute (std::vector & dtensor_, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox) -{ - std::vector dcoord; - std::vector datype, fwd_map, bkw_map; - int nghost_real; - select_real_atoms(fwd_map, bkw_map, nghost_real, dcoord_, datype_, 0, ntypes); - assert(nghost_real == 0); - // resize to nall_real - dcoord.resize(bkw_map.size() * 3); - datype.resize(bkw_map.size()); - // fwd map - select_map(dcoord, dcoord_, fwd_map, 3); - select_map(datype, datype_, fwd_map, 1); - compute_inner(dtensor_, dcoord, datype, dbox); -} - -void -DeepTensor:: -compute (std::vector & dtensor_, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const int nghost, - const InputNlist & lmp_list) -{ - std::vector dcoord; - std::vector datype, fwd_map, bkw_map; - int nghost_real; - select_real_atoms(fwd_map, bkw_map, nghost_real, dcoord_, datype_, nghost, ntypes); - // resize to nall_real - dcoord.resize(bkw_map.size() * 3); - datype.resize(bkw_map.size()); - // fwd map - select_map(dcoord, dcoord_, fwd_map, 3); - select_map(datype, datype_, fwd_map, 1); - // internal nlist - NeighborListData nlist_data; - nlist_data.copy_from_nlist(lmp_list); - nlist_data.shuffle_exclude_empty(fwd_map); - InputNlist nlist; - nlist_data.make_inlist(nlist); - compute_inner(dtensor_, dcoord, datype, dbox, nghost_real, nlist); -} - - -void -DeepTensor:: -compute_inner (std::vector & dtensor_, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox) -{ - int nall = dcoord_.size() / 3; - int nloc = nall; - AtomMap atommap (datype_.begin(), datype_.begin() + nloc); - assert (nloc == atommap.get_type().size()); - - std::vector> input_tensors; - int ret = session_input_tensors (input_tensors, dcoord_, ntypes, datype_, dbox, cell_size, std::vector(), std::vector(), atommap, name_scope); - assert (ret == nloc); - - run_model (dtensor_, session, input_tensors, atommap); -} - -void -DeepTensor:: -compute_inner (std::vector & dtensor_, - const std::vector & dcoord_, - const std::vector & datype_, - const std::vector & dbox, - const int nghost, - const InputNlist & nlist_) -{ - int nall = dcoord_.size() / 3; - int nloc = nall - nghost; - AtomMap atommap (datype_.begin(), datype_.begin() + nloc); - assert (nloc == atommap.get_type().size()); - - NeighborListData nlist_data; - nlist_data.copy_from_nlist(nlist_); - nlist_data.shuffle(atommap); - InputNlist nlist; - nlist_data.make_inlist(nlist); - - std::vector> input_tensors; - int ret = session_input_tensors (input_tensors, dcoord_, ntypes, datype_, dbox, nlist, std::vector(), std::vector(), atommap, nghost, 0, name_scope); - assert (nloc == ret); - - run_model (dtensor_, session, input_tensors, atommap, nghost); -} diff --git a/source/api_cc/src/PaddleDeepPot.cc b/source/api_cc/src/PaddleDeepPot.cc new file mode 100755 index 0000000000..d5f07c76cc --- /dev/null +++ b/source/api_cc/src/PaddleDeepPot.cc @@ -0,0 +1,730 @@ +#include "PaddleDeepPot.h" +#include "AtomMap.h" +#include + +using namespace deepmd; + +static +std::vector cum_sum (const std::vector & n_sel) { + std::vector sec; + sec.resize (n_sel.size() + 1); + sec[0] = 0; + for (int ii = 1; ii < sec.size(); ++ii) { + sec[ii] = sec[ii-1] + n_sel[ii-1]; + } + return sec; +} + +// o_energy: save_infer_model/scale_4.tmp_1 +// o_force: save_infer_model/scale_5.tmp_1 +// o_atom_virial.tmp_0: save_infer_model/scale_1.tmp_1 +static void +paddle_run_model(ENERGYTYPE & dener, + std::vector & dforce_, + std::vector & dvirial, + std::shared_ptr predictor, + const AtomMap& atommap, + const int nghost = 0) +{ + unsigned nloc = atommap.get_type().size(); + unsigned nall = nloc + nghost; + if (nloc == 0) { + dener = 0; + // no backward map needed + // dforce of size nall * 3 + dforce_.resize(nall * 3); + fill(dforce_.begin(), dforce_.end(), 0.0); + // dvirial of size 9 + dvirial.resize(9); + fill(dvirial.begin(), dvirial.end(), 0.0); + return; + } + predictor->Run(); + // Deal with output + // std::vector oe; + std::vector oe; + std::vector of, oav; + auto output_names = predictor->GetOutputNames(); + + auto out_e_t = predictor->GetOutputHandle(output_names[4]); + std::vector out_e_shape = out_e_t->shape(); + int out_e_num = std::accumulate(out_e_shape.begin(), out_e_shape.end(), 1, + std::multiplies()); + oe.resize(out_e_num); + out_e_t->CopyToCpu(oe.data()); + + auto out_f_t = predictor->GetOutputHandle(output_names[5]); + std::vector out_f_shape = out_f_t->shape(); + int out_f_num = std::accumulate(out_f_shape.begin(), out_f_shape.end(), 1, + std::multiplies()); + of.resize(out_f_num); + out_f_t->CopyToCpu(of.data()); + + auto out_v_t = predictor->GetOutputHandle(output_names[1]); + std::vector out_v_shape = out_v_t->shape(); + int out_v_num = std::accumulate(out_v_shape.begin(), out_v_shape.end(), 1, + std::multiplies()); + oav.resize(out_v_num); + out_v_t->CopyToCpu(oav.data()); + + dener = static_cast(oe[0]); + std::vector dforce (3 * nall, 0.0); + dvirial.resize(9); + for (int i = 0; i < nall * 3; ++i) { + dforce[i] = of[i]; + } + for (int i = 0; i < nall; ++i) { + dvirial[0] += 1.0 * oav[9*i+0]; + dvirial[1] += 1.0 * oav[9*i+1]; + dvirial[2] += 1.0 * oav[9*i+2]; + dvirial[3] += 1.0 * oav[9*i+3]; + dvirial[4] += 1.0 * oav[9*i+4]; + dvirial[5] += 1.0 * oav[9*i+5]; + dvirial[6] += 1.0 * oav[9*i+6]; + dvirial[7] += 1.0 * oav[9*i+7]; + dvirial[8] += 1.0 * oav[9*i+8]; + } + dforce_ = dforce; + atommap.backward (dforce_.begin(), dforce.begin(), 3); +} + +// o_energy" save_infer_model/scale_4.tmp_1 +// "o_force" save_infer_model/scale_5.tmp_1 +// "o_atom_energy" save_infer_model/scale_0.tmp_1 +// "o_atom_virial" save_infer_model/scale_1.tmp_1 + +static void +paddle_run_model (deepmd::ENERGYTYPE & dener, + std::vector& dforce_, + std::vector& dvirial, + std::vector& datom_energy_, + std::vector& datom_virial_, + std::shared_ptr predictor, + const deepmd::AtomMap & atommap, + const int& nghost = 0){ + unsigned nloc = atommap.get_type().size(); + unsigned nall = nloc + nghost; + if (nloc == 0) { + dener = 0; + // no backward map needed + // dforce of size nall * 3 + dforce_.resize(nall * 3); + fill(dforce_.begin(), dforce_.end(), 0.0); + // dvirial of size 9 + dvirial.resize(9); + fill(dvirial.begin(), dvirial.end(), 0.0); + // datom_energy_ of size nall + datom_energy_.resize(nall); + fill(datom_energy_.begin(), datom_energy_.end(), 0.0); + // datom_virial_ of size nall * 9 + datom_virial_.resize(nall * 9); + fill(datom_virial_.begin(), datom_virial_.end(), 0.0); + return; + } + predictor->Run(); + // {"o_energy", "o_force", "o_atom_energy", "o_atom_virial"}, + // Deal with output + // std::vector oe; + std::vector oe; + std::vector of, oav, oae; + auto output_names = predictor->GetOutputNames(); + + auto out_e_t = predictor->GetOutputHandle(output_names[4]); + std::vector out_e_shape = out_e_t->shape(); + int out_e_num = std::accumulate(out_e_shape.begin(), out_e_shape.end(), 1, + std::multiplies()); + oe.resize(out_e_num); + out_e_t->CopyToCpu(oe.data()); + + auto out_f_t = predictor->GetOutputHandle(output_names[5]); + std::vector out_f_shape = out_f_t->shape(); + int out_f_num = std::accumulate(out_f_shape.begin(), out_f_shape.end(), 1, + std::multiplies()); + of.resize(out_f_num); + out_f_t->CopyToCpu(of.data()); + + auto out_ae_t = predictor->GetOutputHandle(output_names[0]); + std::vector out_ae_shape = out_ae_t->shape(); + int out_ae_num = std::accumulate(out_ae_shape.begin(), out_ae_shape.end(), 1, + std::multiplies()); + oae.resize(out_ae_num); + out_ae_t->CopyToCpu(oae.data()); + + auto out_av_t = predictor->GetOutputHandle(output_names[1]); + std::vector out_av_shape = out_av_t->shape(); + int out_av_num = std::accumulate(out_av_shape.begin(), out_av_shape.end(), 1, + std::multiplies()); + oav.resize(out_av_num); + out_av_t->CopyToCpu(oav.data()); + + dener = static_cast(oe[0]); + std::vector dforce (3 * nall); + std::vector datom_energy (nall, 0); + std::vector datom_virial (9 * nall); + dvirial.resize (9); + for (int ii = 0; ii < nall * 3; ++ii) { + dforce[ii] = of[ii]; + } + for (int ii = 0; ii < nloc; ++ii) { + datom_energy[ii] = oae[ii]; + } + for (int ii = 0; ii < nall * 9; ++ii) { + datom_virial[ii] = oav[ii]; + } + for (int ii = 0; ii < nall; ++ii) { + dvirial[0] += 1.0 * datom_virial[9*ii+0]; + dvirial[1] += 1.0 * datom_virial[9*ii+1]; + dvirial[2] += 1.0 * datom_virial[9*ii+2]; + dvirial[3] += 1.0 * datom_virial[9*ii+3]; + dvirial[4] += 1.0 * datom_virial[9*ii+4]; + dvirial[5] += 1.0 * datom_virial[9*ii+5]; + dvirial[6] += 1.0 * datom_virial[9*ii+6]; + dvirial[7] += 1.0 * datom_virial[9*ii+7]; + dvirial[8] += 1.0 * datom_virial[9*ii+8]; + } + dforce_ = dforce; + datom_energy_ = datom_energy; + datom_virial_ = datom_virial; + atommap.backward (dforce_.begin(), dforce.begin(), 3); + atommap.backward (datom_energy_.begin(), datom_energy.begin(), 1); + atommap.backward (datom_virial_.begin(), datom_virial.begin(), 9); +} + +PaddleDeepPot::PaddleDeepPot (): inited (false),init_nbor (false) +{ + +} + +PaddleDeepPot::PaddleDeepPot (std::string& prog_str, std::string& params_str) + : inited (false), init_nbor (false) +{ + init(prog_str, params_str); +} + +PaddleDeepPot::~PaddleDeepPot() {} + +void PaddleDeepPot::init (std::string& prog_str, std::string& params_str) +{ + if (inited){ + std::cerr << "Init error" << std::endl; + return ; + } + math_lib_num_threads = deepmd::get_math_lib_num_threads(); + config.SetModelBuffer(prog_str.c_str(), prog_str.size(), params_str.c_str(), params_str.size()); + // config.SwitchIrOptim(); + // config.EnableMKLDNN(); + config.SetCpuMathLibraryNumThreads(math_lib_num_threads); + std::cout<<">>> Info of cpu math lib num threads: "<< deepmd::get_math_lib_num_threads() <("descrpt_attr/rcut") + ntypes = get_scalar("descrpt_attr/ntypes"); + dfparam_ = get_scalar("fitting_attr/dfparam"); + daparam_ = get_scalar("fitting_attr/daparam"); + dfparam_ = dfparam_ < 0 ? 0 : dfparam_; + daparam_ = daparam_ < 0 ? 0 : daparam_; +*/ + cell_size = rcut; + dfparam = 0; + daparam = 0; + if (dfparam < 0) dfparam = 0; + if (daparam < 0) daparam = 0; + + inited = true; + init_nbor = false; +} + +void +PaddleDeepPot:: +print_summary(const std::string &pre) const +{ + std::cout << pre << "installed to: " + global_install_prefix << std::endl; + std::cout << pre << "source: " + global_git_summ << std::endl; + std::cout << pre << "source brach: " + global_git_branch << std::endl; + std::cout << pre << "source commit: " + global_git_hash << std::endl; + std::cout << pre << "source commit at: " + global_git_date << std::endl; + std::cout << pre << "surpport model ver.:" + global_model_version << std::endl; + std::cout << pre << "build float prec: " + global_float_prec << std::endl; + std::cout << pre << "build with paddle inference framework " << std::endl; +} + +void +PaddleDeepPot:: +validate_fparam_aparam(const int & nloc, + const std::vector &fparam, + const std::vector &aparam, + const int &dfparam, + const int &daparam) const +{ + if (fparam.size() != dfparam) { + throw std::runtime_error("the dim of frame parameter provided is not consistent with what the model uses"); + } + if (aparam.size() != daparam * nloc) { + throw std::runtime_error("the dim of atom parameter provided is not consistent with what the model uses"); + } +} + +void PaddleDeepPot::compute (ENERGYTYPE & dener, + std::vector & dforce_, + std::vector & dvirial, + const std::vector & dcoord_, + const std::vector & datype_, + const std::vector & dbox, + const std::vector& fparam, + const std::vector& aparam){ + + // Initialization + int nall = dcoord_.size() / 3; + int nloc = nall; + atommap = deepmd::AtomMap (datype_.begin(), datype_.begin() + nloc); + assert (nloc == atommap.get_type().size()); + validate_fparam_aparam(nloc, fparam, aparam, dfparam, daparam); + + int ret = paddle_input_tensors(predictor, dcoord_, ntypes, datype_, dbox, cell_size, fparam, aparam, atommap); + // Execution + assert (ret == nloc); + + paddle_run_model(dener, dforce_, dvirial, predictor, atommap); +} + +void +PaddleDeepPot:: +compute (ENERGYTYPE & dener, + std::vector & dforce_, + std::vector & dvirial, + const std::vector & dcoord_, + const std::vector & datype_, + const std::vector & dbox, + const int nghost, + const InputNlist & lmp_list, + const int& ago, + const std::vector & fparam, + const std::vector & aparam_) +{ + std::vector dcoord, dforce, aparam; + std::vector datype, fwd_map, bkw_map; + int nghost_real; + select_real_atoms(fwd_map, bkw_map, nghost_real, dcoord_, datype_, nghost, ntypes); + // resize to nall_real + dcoord.resize(bkw_map.size() * 3); + datype.resize(bkw_map.size()); + // fwd map + select_map(dcoord, dcoord_, fwd_map, 3); + select_map(datype, datype_, fwd_map, 1); + // aparam + if (daparam > 0){ + aparam.resize(bkw_map.size()); + select_map(aparam, aparam_, fwd_map, daparam); + } + // internal nlist + if (ago == 0){ + nlist_data.copy_from_nlist(lmp_list); + nlist_data.shuffle_exclude_empty(fwd_map); + } + compute_inner(dener, dforce, dvirial, dcoord, datype, dbox, nghost_real, ago, fparam, aparam); + // bkw map + dforce_.resize(fwd_map.size() * 3); + select_map(dforce_, dforce, bkw_map, 3); +} + + +void +PaddleDeepPot:: +compute (ENERGYTYPE & dener, + std::vector & dforce_, + std::vector & dvirial, + std::vector & datom_energy_, + std::vector & datom_virial_, + const std::vector & dcoord_, + const std::vector & datype_, + const std::vector & dbox, + const std::vector & fparam, + const std::vector & aparam) +{ + atommap = deepmd::AtomMap (datype_.begin(), datype_.end()); + validate_fparam_aparam(atommap.get_type().size(), fparam, aparam, dfparam, daparam); + + int nloc = paddle_input_tensors (predictor, dcoord_, ntypes, datype_, dbox, cell_size, fparam, aparam, atommap); + + paddle_run_model (dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, atommap); +} + +void +PaddleDeepPot:: +compute (ENERGYTYPE & dener, + std::vector & dforce_, + std::vector & dvirial, + std::vector & datom_energy_, + std::vector & datom_virial_, + const std::vector & dcoord_, + const std::vector & datype_, + const std::vector & dbox, + const int nghost, + const InputNlist & lmp_list, + const int & ago, + const std::vector & fparam, + const std::vector & aparam) +{ + int nall = dcoord_.size() / 3; + int nloc = nall - nghost; + validate_fparam_aparam(nloc, fparam, aparam, dfparam, daparam); + if (ago == 0) { + atommap = AtomMap (datype_.begin(), datype_.begin() + nloc); + assert (nloc == atommap.get_type().size()); + + nlist_data.copy_from_nlist(lmp_list); + nlist_data.shuffle(atommap); + nlist_data.make_inlist(nlist); + } + int ret = paddle_input_tensors (predictor, dcoord_, ntypes, datype_, dbox, nlist, fparam, aparam, atommap, nghost, ago); + assert (nloc == ret); + paddle_run_model (dener, dforce_, dvirial, datom_energy_, datom_virial_, predictor, atommap, nghost); +} + +void +PaddleDeepPot:: +compute_inner (ENERGYTYPE & dener, + std::vector & dforce_, + std::vector & dvirial, + const std::vector & dcoord_, + const std::vector & datype_, + const std::vector & dbox, + const int nghost, + const int& ago, + const std::vector & fparam, + const std::vector & aparam) +{ + int nall = dcoord_.size() / 3; + int nloc = nall - nghost; + + validate_fparam_aparam(nloc, fparam, aparam, dfparam, daparam); + + // agp == 0 means that the LAMMPS nbor list has been updated + if (ago == 0) { + atommap = deepmd::AtomMap (datype_.begin(), datype_.begin() + nloc); + assert (nloc == atommap.get_type().size()); + nlist_data.shuffle(atommap); + nlist_data.make_inlist(nlist); + } + int ret = paddle_input_tensors (predictor, dcoord_, ntypes, datype_, dbox, nlist, fparam, aparam, atommap, nghost, ago); + assert (nloc == ret); + paddle_run_model (dener, dforce_, dvirial, predictor, atommap, nghost); +} + +PaddleDeepPotModelDevi::PaddleDeepPotModelDevi () + : inited (false), + init_nbor (false), + numb_models (0) +{ + +} + +PaddleDeepPotModelDevi::PaddleDeepPotModelDevi (std::vector & prog_strs, std::vector & params_strs) +{ + init(prog_strs, params_strs); +} + +PaddleDeepPotModelDevi::~PaddleDeepPotModelDevi() {} + +void PaddleDeepPotModelDevi::init (std::vector& prog_strs, std::vector& params_strs){ + if (inited){ + std::cerr << "Init error" << std::endl; + return ; + } + numb_models = prog_strs.size(); + configs.resize(numb_models); + predictors.resize(numb_models); + math_lib_num_threads = deepmd::get_math_lib_num_threads(); + for (unsigned ii = 0; ii < numb_models; ++ii){ + configs[ii].SetModelBuffer(prog_strs[ii].c_str(), prog_strs[ii].size(), params_strs[ii].c_str(), params_strs[ii].size()); + configs[ii].EnableMKLDNN(); + configs[ii].SetCpuMathLibraryNumThreads(math_lib_num_threads); + configs[ii].SwitchIrOptim(); + configs[ii].EnableMemoryOptim(); + predictors[ii] = paddle_infer::CreatePredictor(configs[ii]); + } + + /* + rcut = get_scalar("descrpt_attr/rcut") + ntypes = get_scalar("descrpt_attr/ntypes"); + dfparam_ = get_scalar("fitting_attr/dfparam"); + daparam_ = get_scalar("fitting_attr/daparam"); + dfparam_ = dfparam_ < 0 ? 0 : dfparam_; + daparam_ = daparam_ < 0 ? 0 : daparam_; + */ + cell_size = rcut; + dfparam = 0; + daparam = 0; + if (dfparam < 0) dfparam = 0; + if (daparam < 0) daparam = 0; + + inited = true; + init_nbor = false; +} + +void +PaddleDeepPotModelDevi:: +cum_sum (const std::vector > n_sel) +{ + for (int ii = 0; ii < numb_models; ++ii) { + std::vector _sec; + _sec.resize (n_sel[ii].size() + 1); + _sec[0] = 0; + for (int jj = 1; jj < _sec.size(); ++jj) { + _sec[jj] = _sec[jj-1] + n_sel[ii][jj-1]; + } + sec.push_back(_sec); + } +} + +void +PaddleDeepPotModelDevi:: +validate_fparam_aparam(const int & nloc, + const std::vector &fparam, + const std::vector &aparam, + const int &dfparam, + const int &daparam) const +{ + if (fparam.size() != dfparam) { + throw std::runtime_error("the dim of frame parameter provided is not consistent with what the model uses"); + } + if (aparam.size() != daparam * nloc) { + throw std::runtime_error("the dim of atom parameter provided is not consistent with what the model uses"); + } +} + +void +PaddleDeepPotModelDevi:: +compute (std::vector & all_energy, + std::vector> & all_force, + std::vector> & all_virial, + const std::vector & dcoord_, + const std::vector & datype_, + const std::vector & dbox, + const int nghost, + const InputNlist & lmp_list, + const int & ago, + const std::vector & fparam, + const std::vector & aparam) +{ + if (numb_models == 0) return; + int nall = dcoord_.size() / 3; + int nloc = nall - nghost; + validate_fparam_aparam(nloc, fparam, aparam, dfparam, daparam); + + // agp == 0 means that the LAMMPS nbor list has been updated + if (ago == 0) { + atommap = AtomMap (datype_.begin(), datype_.begin() + nloc); + assert (nloc == atommap.get_type().size()); + + nlist_data.copy_from_nlist(lmp_list); + nlist_data.shuffle(atommap); + nlist_data.make_inlist(nlist); + } + + std::vector ret(numb_models, 0); + for (unsigned ii = 0; ii < numb_models; ++ii){ + ret[ii] = paddle_input_tensors (predictors[ii], dcoord_, ntypes, datype_, dbox, nlist, fparam, aparam, atommap, nghost, ago); + } + + all_energy.resize (numb_models); + all_force.resize (numb_models); + all_virial.resize (numb_models); + for (unsigned ii = 0; ii < numb_models; ++ii){ + assert (nloc == ret[ii]); + } + for (unsigned ii = 0; ii < numb_models; ++ii) { + // paddle_run_model (all_energy[ii], all_force[ii], all_virial[ii], sessions[ii], predictor_, atommap, nghost); // we may need to open new context + paddle_run_model (all_energy[ii], all_force[ii], all_virial[ii], predictors[ii], atommap, nghost); + } +} + +void +PaddleDeepPotModelDevi:: +compute (std::vector & all_energy, + std::vector> & all_force, + std::vector> & all_virial, + std::vector> & all_atom_energy, + std::vector> & all_atom_virial, + const std::vector & dcoord_, + const std::vector & datype_, + const std::vector & dbox, + const int nghost, + const InputNlist & lmp_list, + const int & ago, + const std::vector & fparam, + const std::vector & aparam) +{ + if (numb_models == 0) return; + int nall = dcoord_.size() / 3; + int nloc = nall - nghost; + validate_fparam_aparam(nloc, fparam, aparam, dfparam, daparam); + + // agp == 0 means that the LAMMPS nbor list has been updated + if (ago == 0) { + atommap = AtomMap (datype_.begin(), datype_.begin() + nloc); + assert (nloc == atommap.get_type().size()); + + nlist_data.copy_from_nlist(lmp_list); + nlist_data.shuffle(atommap); + nlist_data.make_inlist(nlist); + } + + std::vector ret(numb_models, 0); + for (int ii = 0; ii < numb_models; ++ii){ + ret[ii] = paddle_input_tensors (predictors[ii], dcoord_, ntypes, datype_, dbox, nlist, fparam, aparam, atommap, nghost, ago); + } + + all_energy.resize (numb_models); + all_force .resize (numb_models); + all_virial.resize (numb_models); + all_atom_energy.resize (numb_models); + all_atom_virial.resize (numb_models); + for (unsigned ii = 0; ii < numb_models; ++ii){ + assert (nloc == ret[ii]); + } + for (unsigned ii = 0; ii < numb_models; ++ii) { + paddle_run_model (all_energy[ii], all_force[ii], all_virial[ii], all_atom_energy[ii], all_atom_virial[ii], predictors[ii], atommap, nghost); + } +} + +#ifndef HIGH_PREC +void +PaddleDeepPotModelDevi:: +compute_avg (ENERGYTYPE & dener, + const std::vector& all_energy) +{ + assert (all_energy.size() == numb_models); + if (numb_models == 0) return; + + dener = 0; + for (unsigned ii = 0; ii < numb_models; ++ii){ + dener += all_energy[ii]; + } + dener /= (ENERGYTYPE)(numb_models); +} +#endif + +void +PaddleDeepPotModelDevi:: +compute_avg (VALUETYPE & dener, + const std::vector & all_energy) +{ + assert (all_energy.size() == numb_models); + if (numb_models == 0) return; + + dener = 0; + for (unsigned ii = 0; ii < numb_models; ++ii){ + dener += all_energy[ii]; + } + dener /= (VALUETYPE)(numb_models); +} + +void +PaddleDeepPotModelDevi:: +compute_avg (std::vector & avg, + const std::vector > & xx) +{ + assert (xx.size() == numb_models); + if (numb_models == 0) return; + + avg.resize(xx[0].size()); + fill (avg.begin(), avg.end(), VALUETYPE(0.)); + + for (unsigned ii = 0; ii < numb_models; ++ii){ + for (unsigned jj = 0; jj < avg.size(); ++jj){ + avg[jj] += xx[ii][jj]; + } + } + + for (unsigned jj = 0; jj < avg.size(); ++jj){ + avg[jj] /= VALUETYPE(numb_models); + } +} + +void +PaddleDeepPotModelDevi:: +compute_std_e (std::vector & std, + const std::vector & avg, + const std::vector >&xx) +{ + assert (xx.size() == numb_models); + if (numb_models == 0) return; + + unsigned ndof = avg.size(); + unsigned nloc = ndof; + assert (nloc == ndof); + + std.resize(nloc); + fill (std.begin(), std.end(), VALUETYPE(0.)); + + for (unsigned ii = 0; ii < numb_models; ++ii) { + for (unsigned jj = 0 ; jj < nloc; ++jj){ + const VALUETYPE * tmp_f = &(xx[ii][jj]); + const VALUETYPE * tmp_avg = &(avg[jj]); + VALUETYPE vdiff = xx[ii][jj] - avg[jj]; + std[jj] += vdiff * vdiff; + } + } + + for (unsigned jj = 0; jj < nloc; ++jj){ + std[jj] = sqrt(std[jj] / VALUETYPE(numb_models)); + // std[jj] = sqrt(std[jj] / VALUETYPE(numb_models-1)); + } +} + +void +PaddleDeepPotModelDevi:: +compute_std_f (std::vector & std, + const std::vector & avg, + const std::vector >&xx) +{ + assert (xx.size() == numb_models); + if (numb_models == 0) return; + + unsigned ndof = avg.size(); + unsigned nloc = ndof / 3; + assert (nloc * 3 == ndof); + + std.resize(nloc); + fill (std.begin(), std.end(), VALUETYPE(0.)); + + for (unsigned ii = 0; ii < numb_models; ++ii) { + for (unsigned jj = 0 ; jj < nloc; ++jj){ + const VALUETYPE * tmp_f = &(xx[ii][jj*3]); + const VALUETYPE * tmp_avg = &(avg[jj*3]); + VALUETYPE vdiff[3]; + vdiff[0] = tmp_f[0] - tmp_avg[0]; + vdiff[1] = tmp_f[1] - tmp_avg[1]; + vdiff[2] = tmp_f[2] - tmp_avg[2]; + std[jj] += deepmd::dot3(vdiff, vdiff); + } + } + + for (unsigned jj = 0; jj < nloc; ++jj){ + std[jj] = sqrt(std[jj] / VALUETYPE(numb_models)); + // std[jj] = sqrt(std[jj] / VALUETYPE(numb_models-1)); + } +} + +void +PaddleDeepPotModelDevi:: +compute_relative_std_f (std::vector &std, + const std::vector &avg, + const VALUETYPE eps) +{ + unsigned nloc = std.size(); + for (unsigned ii = 0; ii < nloc; ++ii){ + const VALUETYPE * tmp_avg = &(avg[ii*3]); + VALUETYPE vdiff[3]; + vdiff[0] = tmp_avg[0]; + vdiff[1] = tmp_avg[1]; + vdiff[2] = tmp_avg[2]; + VALUETYPE f_norm = sqrt(deepmd::dot3(vdiff, vdiff)); + // relative std = std/(abs(f)+eps) + std[ii] /= f_norm + eps; + } +} + + diff --git a/source/api_cc/src/common.cc b/source/api_cc/src/common.cc old mode 100644 new mode 100755 index 74c317529e..5383541275 --- a/source/api_cc/src/common.cc +++ b/source/api_cc/src/common.cc @@ -2,7 +2,7 @@ #include "AtomMap.h" #include "device.h" -using namespace tensorflow; +// using namespace tensorflow; static std::vector split(const std::string &input_, @@ -196,14 +196,14 @@ make_inlist(InputNlist & inlist) inlist.firstneigh = &firstneigh[0]; } -void -deepmd:: -check_status(const tensorflow::Status& status) { - if (!status.ok()) { - std::cout << status.ToString() << std::endl; - exit(1); - } -} +// void +// deepmd:: +// check_status(const tensorflow::Status& status) { +// if (!status.ok()) { +// std::cout << status.ToString() << std::endl; +// exit(1); +// } +// } void deepmd:: @@ -239,314 +239,314 @@ name_prefix(const std::string & scope) return prefix; } -int -deepmd:: -session_input_tensors ( - std::vector> & input_tensors, - const std::vector & dcoord_, - const int & ntypes, - const std::vector & datype_, - const std::vector & dbox, - const deepmd::VALUETYPE & cell_size, - const std::vector & fparam_, - const std::vector & aparam_, - const deepmd::AtomMap& atommap, - const std::string scope) -{ - bool b_pbc = (dbox.size() == 9); - - int nframes = 1; - int nall = dcoord_.size() / 3; - int nloc = nall; - assert (nall == datype_.size()); - - std::vector datype = atommap.get_type(); - std::vector type_count (ntypes, 0); - for (unsigned ii = 0; ii < datype.size(); ++ii){ - type_count[datype[ii]] ++; - } - datype.insert (datype.end(), datype_.begin() + nloc, datype_.end()); - - TensorShape coord_shape ; - coord_shape.AddDim (nframes); - coord_shape.AddDim (nall * 3); - TensorShape type_shape ; - type_shape.AddDim (nframes); - type_shape.AddDim (nall); - TensorShape box_shape ; - box_shape.AddDim (nframes); - box_shape.AddDim (9); - TensorShape mesh_shape ; - if (b_pbc){ - mesh_shape.AddDim(6); - } - else { - mesh_shape.AddDim(0); - } - TensorShape natoms_shape ; - natoms_shape.AddDim (2 + ntypes); - TensorShape fparam_shape ; - fparam_shape.AddDim (nframes); - fparam_shape.AddDim (fparam_.size()); - TensorShape aparam_shape ; - aparam_shape.AddDim (nframes); - aparam_shape.AddDim (aparam_.size()); +// int +// deepmd:: +// session_input_tensors ( +// std::vector> & input_tensors, +// const std::vector & dcoord_, +// const int & ntypes, +// const std::vector & datype_, +// const std::vector & dbox, +// const deepmd::VALUETYPE & cell_size, +// const std::vector & fparam_, +// const std::vector & aparam_, +// const deepmd::AtomMap& atommap, +// const std::string scope) +// { +// bool b_pbc = (dbox.size() == 9); + +// int nframes = 1; +// int nall = dcoord_.size() / 3; +// int nloc = nall; +// assert (nall == datype_.size()); + +// std::vector datype = atommap.get_type(); +// std::vector type_count (ntypes, 0); +// for (unsigned ii = 0; ii < datype.size(); ++ii){ +// type_count[datype[ii]] ++; +// } +// datype.insert (datype.end(), datype_.begin() + nloc, datype_.end()); + +// TensorShape coord_shape ; +// coord_shape.AddDim (nframes); +// coord_shape.AddDim (nall * 3); +// TensorShape type_shape ; +// type_shape.AddDim (nframes); +// type_shape.AddDim (nall); +// TensorShape box_shape ; +// box_shape.AddDim (nframes); +// box_shape.AddDim (9); +// TensorShape mesh_shape ; +// if (b_pbc){ +// mesh_shape.AddDim(6); +// } +// else { +// mesh_shape.AddDim(0); +// } +// TensorShape natoms_shape ; +// natoms_shape.AddDim (2 + ntypes); +// TensorShape fparam_shape ; +// fparam_shape.AddDim (nframes); +// fparam_shape.AddDim (fparam_.size()); +// TensorShape aparam_shape ; +// aparam_shape.AddDim (nframes); +// aparam_shape.AddDim (aparam_.size()); -#ifdef HIGH_PREC - Tensor coord_tensor (DT_DOUBLE, coord_shape); - Tensor box_tensor (DT_DOUBLE, box_shape); - Tensor fparam_tensor (DT_DOUBLE, fparam_shape); - Tensor aparam_tensor (DT_DOUBLE, aparam_shape); -#else - Tensor coord_tensor (DT_FLOAT, coord_shape); - Tensor box_tensor (DT_FLOAT, box_shape); - Tensor fparam_tensor (DT_FLOAT, fparam_shape); - Tensor aparam_tensor (DT_FLOAT, aparam_shape); -#endif - Tensor type_tensor (DT_INT32, type_shape); - Tensor mesh_tensor (DT_INT32, mesh_shape); - Tensor natoms_tensor (DT_INT32, natoms_shape); - - auto coord = coord_tensor.matrix (); - auto type = type_tensor.matrix (); - auto box = box_tensor.matrix (); - auto mesh = mesh_tensor.flat (); - auto natoms = natoms_tensor.flat (); - auto fparam = fparam_tensor.matrix (); - auto aparam = aparam_tensor.matrix (); - - std::vector dcoord (dcoord_); - atommap.forward (dcoord.begin(), dcoord_.begin(), 3); +// #ifdef HIGH_PREC +// Tensor coord_tensor (DT_DOUBLE, coord_shape); +// Tensor box_tensor (DT_DOUBLE, box_shape); +// Tensor fparam_tensor (DT_DOUBLE, fparam_shape); +// Tensor aparam_tensor (DT_DOUBLE, aparam_shape); +// #else +// Tensor coord_tensor (DT_FLOAT, coord_shape); +// Tensor box_tensor (DT_FLOAT, box_shape); +// Tensor fparam_tensor (DT_FLOAT, fparam_shape); +// Tensor aparam_tensor (DT_FLOAT, aparam_shape); +// #endif +// Tensor type_tensor (DT_INT32, type_shape); +// Tensor mesh_tensor (DT_INT32, mesh_shape); +// Tensor natoms_tensor (DT_INT32, natoms_shape); + +// auto coord = coord_tensor.matrix (); +// auto type = type_tensor.matrix (); +// auto box = box_tensor.matrix (); +// auto mesh = mesh_tensor.flat (); +// auto natoms = natoms_tensor.flat (); +// auto fparam = fparam_tensor.matrix (); +// auto aparam = aparam_tensor.matrix (); + +// std::vector dcoord (dcoord_); +// atommap.forward (dcoord.begin(), dcoord_.begin(), 3); - for (int ii = 0; ii < nframes; ++ii){ - for (int jj = 0; jj < nall * 3; ++jj){ - coord(ii, jj) = dcoord[jj]; - } - if(b_pbc){ - for (int jj = 0; jj < 9; ++jj){ - box(ii, jj) = dbox[jj]; - } - } - else{ - for (int jj = 0; jj < 9; ++jj){ - box(ii, jj) = 0.; - } - } - for (int jj = 0; jj < nall; ++jj){ - type(ii, jj) = datype[jj]; - } - for (int jj = 0; jj < fparam_.size(); ++jj){ - fparam(ii, jj) = fparam_[jj]; - } - for (int jj = 0; jj < aparam_.size(); ++jj){ - aparam(ii, jj) = aparam_[jj]; - } - } - if (b_pbc){ - mesh (1-1) = 0; - mesh (2-1) = 0; - mesh (3-1) = 0; - mesh (4-1) = 0; - mesh (5-1) = 0; - mesh (6-1) = 0; - } - natoms (0) = nloc; - natoms (1) = nall; - for (int ii = 0; ii < ntypes; ++ii) natoms(ii+2) = type_count[ii]; - - std::string prefix = ""; - if (scope != ""){ - prefix = scope + "/"; - } - input_tensors = { - {prefix+"t_coord", coord_tensor}, - {prefix+"t_type", type_tensor}, - {prefix+"t_box", box_tensor}, - {prefix+"t_mesh", mesh_tensor}, - {prefix+"t_natoms", natoms_tensor}, - }; - if (fparam_.size() > 0) { - input_tensors.push_back({prefix+"t_fparam", fparam_tensor}); - } - if (aparam_.size() > 0) { - input_tensors.push_back({prefix+"t_aparam", aparam_tensor}); - } - return nloc; -} - -int -deepmd:: -session_input_tensors ( - std::vector> & input_tensors, - const std::vector & dcoord_, - const int & ntypes, - const std::vector & datype_, - const std::vector & dbox, - InputNlist & dlist, - const std::vector & fparam_, - const std::vector & aparam_, - const deepmd::AtomMap& atommap, - const int nghost, - const int ago, - const std::string scope) -{ - assert (dbox.size() == 9); - - int nframes = 1; - int nall = dcoord_.size() / 3; - int nloc = nall - nghost; - assert (nall == datype_.size()); - - std::vector datype = atommap.get_type(); - std::vector type_count (ntypes, 0); - for (unsigned ii = 0; ii < datype.size(); ++ii){ - type_count[datype[ii]] ++; - } - datype.insert (datype.end(), datype_.begin() + nloc, datype_.end()); - - TensorShape coord_shape ; - coord_shape.AddDim (nframes); - coord_shape.AddDim (nall * 3); - TensorShape type_shape ; - type_shape.AddDim (nframes); - type_shape.AddDim (nall); - TensorShape box_shape ; - box_shape.AddDim (nframes); - box_shape.AddDim (9); - TensorShape mesh_shape ; - mesh_shape.AddDim (16); - TensorShape natoms_shape ; - natoms_shape.AddDim (2 + ntypes); - TensorShape fparam_shape ; - fparam_shape.AddDim (nframes); - fparam_shape.AddDim (fparam_.size()); - TensorShape aparam_shape ; - aparam_shape.AddDim (nframes); - aparam_shape.AddDim (aparam_.size()); +// for (int ii = 0; ii < nframes; ++ii){ +// for (int jj = 0; jj < nall * 3; ++jj){ +// coord(ii, jj) = dcoord[jj]; +// } +// if(b_pbc){ +// for (int jj = 0; jj < 9; ++jj){ +// box(ii, jj) = dbox[jj]; +// } +// } +// else{ +// for (int jj = 0; jj < 9; ++jj){ +// box(ii, jj) = 0.; +// } +// } +// for (int jj = 0; jj < nall; ++jj){ +// type(ii, jj) = datype[jj]; +// } +// for (int jj = 0; jj < fparam_.size(); ++jj){ +// fparam(ii, jj) = fparam_[jj]; +// } +// for (int jj = 0; jj < aparam_.size(); ++jj){ +// aparam(ii, jj) = aparam_[jj]; +// } +// } +// if (b_pbc){ +// mesh (1-1) = 0; +// mesh (2-1) = 0; +// mesh (3-1) = 0; +// mesh (4-1) = 0; +// mesh (5-1) = 0; +// mesh (6-1) = 0; +// } +// natoms (0) = nloc; +// natoms (1) = nall; +// for (int ii = 0; ii < ntypes; ++ii) natoms(ii+2) = type_count[ii]; + +// std::string prefix = ""; +// if (scope != ""){ +// prefix = scope + "/"; +// } +// input_tensors = { +// {prefix+"t_coord", coord_tensor}, +// {prefix+"t_type", type_tensor}, +// {prefix+"t_box", box_tensor}, +// {prefix+"t_mesh", mesh_tensor}, +// {prefix+"t_natoms", natoms_tensor}, +// }; +// if (fparam_.size() > 0) { +// input_tensors.push_back({prefix+"t_fparam", fparam_tensor}); +// } +// if (aparam_.size() > 0) { +// input_tensors.push_back({prefix+"t_aparam", aparam_tensor}); +// } +// return nloc; +// } + +// int +// deepmd:: +// session_input_tensors ( +// std::vector> & input_tensors, +// const std::vector & dcoord_, +// const int & ntypes, +// const std::vector & datype_, +// const std::vector & dbox, +// InputNlist & dlist, +// const std::vector & fparam_, +// const std::vector & aparam_, +// const deepmd::AtomMap& atommap, +// const int nghost, +// const int ago, +// const std::string scope) +// { +// assert (dbox.size() == 9); + +// int nframes = 1; +// int nall = dcoord_.size() / 3; +// int nloc = nall - nghost; +// assert (nall == datype_.size()); + +// std::vector datype = atommap.get_type(); +// std::vector type_count (ntypes, 0); +// for (unsigned ii = 0; ii < datype.size(); ++ii){ +// type_count[datype[ii]] ++; +// } +// datype.insert (datype.end(), datype_.begin() + nloc, datype_.end()); + +// TensorShape coord_shape ; +// coord_shape.AddDim (nframes); +// coord_shape.AddDim (nall * 3); +// TensorShape type_shape ; +// type_shape.AddDim (nframes); +// type_shape.AddDim (nall); +// TensorShape box_shape ; +// box_shape.AddDim (nframes); +// box_shape.AddDim (9); +// TensorShape mesh_shape ; +// mesh_shape.AddDim (16); +// TensorShape natoms_shape ; +// natoms_shape.AddDim (2 + ntypes); +// TensorShape fparam_shape ; +// fparam_shape.AddDim (nframes); +// fparam_shape.AddDim (fparam_.size()); +// TensorShape aparam_shape ; +// aparam_shape.AddDim (nframes); +// aparam_shape.AddDim (aparam_.size()); -#ifdef HIGH_PREC - Tensor coord_tensor (DT_DOUBLE, coord_shape); - Tensor box_tensor (DT_DOUBLE, box_shape); - Tensor fparam_tensor (DT_DOUBLE, fparam_shape); - Tensor aparam_tensor (DT_DOUBLE, aparam_shape); -#else - Tensor coord_tensor (DT_FLOAT, coord_shape); - Tensor box_tensor (DT_FLOAT, box_shape); - Tensor fparam_tensor (DT_FLOAT, fparam_shape); - Tensor aparam_tensor (DT_FLOAT, aparam_shape); -#endif - Tensor type_tensor (DT_INT32, type_shape); - Tensor mesh_tensor (DT_INT32, mesh_shape); - Tensor natoms_tensor (DT_INT32, natoms_shape); - - auto coord = coord_tensor.matrix (); - auto type = type_tensor.matrix (); - auto box = box_tensor.matrix (); - auto mesh = mesh_tensor.flat (); - auto natoms = natoms_tensor.flat (); - auto fparam = fparam_tensor.matrix (); - auto aparam = aparam_tensor.matrix (); - - std::vector dcoord (dcoord_); - atommap.forward (dcoord.begin(), dcoord_.begin(), 3); +// #ifdef HIGH_PREC +// Tensor coord_tensor (DT_DOUBLE, coord_shape); +// Tensor box_tensor (DT_DOUBLE, box_shape); +// Tensor fparam_tensor (DT_DOUBLE, fparam_shape); +// Tensor aparam_tensor (DT_DOUBLE, aparam_shape); +// #else +// Tensor coord_tensor (DT_FLOAT, coord_shape); +// Tensor box_tensor (DT_FLOAT, box_shape); +// Tensor fparam_tensor (DT_FLOAT, fparam_shape); +// Tensor aparam_tensor (DT_FLOAT, aparam_shape); +// #endif +// Tensor type_tensor (DT_INT32, type_shape); +// Tensor mesh_tensor (DT_INT32, mesh_shape); +// Tensor natoms_tensor (DT_INT32, natoms_shape); + +// auto coord = coord_tensor.matrix (); +// auto type = type_tensor.matrix (); +// auto box = box_tensor.matrix (); +// auto mesh = mesh_tensor.flat (); +// auto natoms = natoms_tensor.flat (); +// auto fparam = fparam_tensor.matrix (); +// auto aparam = aparam_tensor.matrix (); + +// std::vector dcoord (dcoord_); +// atommap.forward (dcoord.begin(), dcoord_.begin(), 3); - for (int ii = 0; ii < nframes; ++ii){ - for (int jj = 0; jj < nall * 3; ++jj){ - coord(ii, jj) = dcoord[jj]; - } - for (int jj = 0; jj < 9; ++jj){ - box(ii, jj) = dbox[jj]; - } - for (int jj = 0; jj < nall; ++jj){ - type(ii, jj) = datype[jj]; - } - for (int jj = 0; jj < fparam_.size(); ++jj){ - fparam(ii, jj) = fparam_[jj]; - } - for (int jj = 0; jj < aparam_.size(); ++jj){ - aparam(ii, jj) = aparam_[jj]; - } - } +// for (int ii = 0; ii < nframes; ++ii){ +// for (int jj = 0; jj < nall * 3; ++jj){ +// coord(ii, jj) = dcoord[jj]; +// } +// for (int jj = 0; jj < 9; ++jj){ +// box(ii, jj) = dbox[jj]; +// } +// for (int jj = 0; jj < nall; ++jj){ +// type(ii, jj) = datype[jj]; +// } +// for (int jj = 0; jj < fparam_.size(); ++jj){ +// fparam(ii, jj) = fparam_[jj]; +// } +// for (int jj = 0; jj < aparam_.size(); ++jj){ +// aparam(ii, jj) = aparam_[jj]; +// } +// } - for (int ii = 0; ii < 16; ++ii) mesh(ii) = 0; +// for (int ii = 0; ii < 16; ++ii) mesh(ii) = 0; - const int stride = sizeof(int *) / sizeof(int); - assert (stride * sizeof(int) == sizeof(int *)); - assert (stride <= 4); - mesh (0) = ago; - mesh (1) = dlist.inum; - mesh (2) = 0; - mesh (3) = 0; - memcpy (&mesh(4), &(dlist.ilist), sizeof(int *)); - memcpy (&mesh(8), &(dlist.numneigh), sizeof(int *)); - memcpy (&mesh(12), &(dlist.firstneigh), sizeof(int **)); - - natoms (0) = nloc; - natoms (1) = nall; - for (int ii = 0; ii < ntypes; ++ii) natoms(ii+2) = type_count[ii]; - - std::string prefix = ""; - if (scope != ""){ - prefix = scope + "/"; - } - input_tensors = { - {prefix+"t_coord", coord_tensor}, - {prefix+"t_type", type_tensor}, - {prefix+"t_box", box_tensor}, - {prefix+"t_mesh", mesh_tensor}, - {prefix+"t_natoms",natoms_tensor}, - }; - if (fparam_.size() > 0) { - input_tensors.push_back({prefix+"t_fparam", fparam_tensor}); - } - if (aparam_.size() > 0) { - input_tensors.push_back({prefix+"t_aparam", aparam_tensor}); - } - return nloc; -} - -template -VT -deepmd:: -session_get_scalar(Session* session, const std::string name_, const std::string scope) -{ - std::string name = name_; - if (scope != "") { - name = scope + "/" + name; - } - std::vector output_tensors; - deepmd::check_status (session->Run(std::vector> ({}), - {name.c_str()}, - {}, - &output_tensors)); - Tensor output_rc = output_tensors[0]; - auto orc = output_rc.flat (); - return orc(0); -} - -template -void -deepmd:: -session_get_vector(std::vector & o_vec, Session* session, const std::string name_, const std::string scope) -{ - std::string name = name_; - if (scope != "") { - name = scope + "/" + name; - } - std::vector output_tensors; - deepmd::check_status (session->Run(std::vector> ({}), - {name.c_str()}, - {}, - &output_tensors)); - Tensor output_rc = output_tensors[0]; - assert(1 == output_rc.shape().dims()); - int dof = output_rc.shape().dim_size(0); - o_vec.resize(dof); - auto orc = output_rc.flat (); - for (int ii = 0; ii < dof; ++ii){ - o_vec[ii] = orc(ii); - } -} +// const int stride = sizeof(int *) / sizeof(int); +// assert (stride * sizeof(int) == sizeof(int *)); +// assert (stride <= 4); +// mesh (0) = ago; +// mesh (1) = dlist.inum; +// mesh (2) = 0; +// mesh (3) = 0; +// memcpy (&mesh(4), &(dlist.ilist), sizeof(int *)); +// memcpy (&mesh(8), &(dlist.numneigh), sizeof(int *)); +// memcpy (&mesh(12), &(dlist.firstneigh), sizeof(int **)); + +// natoms (0) = nloc; +// natoms (1) = nall; +// for (int ii = 0; ii < ntypes; ++ii) natoms(ii+2) = type_count[ii]; + +// std::string prefix = ""; +// if (scope != ""){ +// prefix = scope + "/"; +// } +// input_tensors = { +// {prefix+"t_coord", coord_tensor}, +// {prefix+"t_type", type_tensor}, +// {prefix+"t_box", box_tensor}, +// {prefix+"t_mesh", mesh_tensor}, +// {prefix+"t_natoms",natoms_tensor}, +// }; +// if (fparam_.size() > 0) { +// input_tensors.push_back({prefix+"t_fparam", fparam_tensor}); +// } +// if (aparam_.size() > 0) { +// input_tensors.push_back({prefix+"t_aparam", aparam_tensor}); +// } +// return nloc; +// } + +// template +// VT +// deepmd:: +// session_get_scalar(Session* session, const std::string name_, const std::string scope) +// { +// std::string name = name_; +// if (scope != "") { +// name = scope + "/" + name; +// } +// std::vector output_tensors; +// deepmd::check_status (session->Run(std::vector> ({}), +// {name.c_str()}, +// {}, +// &output_tensors)); +// Tensor output_rc = output_tensors[0]; +// auto orc = output_rc.flat (); +// return orc(0); +// } + +// template +// void +// deepmd:: +// session_get_vector(std::vector & o_vec, Session* session, const std::string name_, const std::string scope) +// { +// std::string name = name_; +// if (scope != "") { +// name = scope + "/" + name; +// } +// std::vector output_tensors; +// deepmd::check_status (session->Run(std::vector> ({}), +// {name.c_str()}, +// {}, +// &output_tensors)); +// Tensor output_rc = output_tensors[0]; +// assert(1 == output_rc.shape().dims()); +// int dof = output_rc.shape().dim_size(0); +// o_vec.resize(dof); +// auto orc = output_rc.flat (); +// for (int ii = 0; ii < dof; ++ii){ +// o_vec[ii] = orc(ii); +// } +// } template @@ -575,15 +575,15 @@ select_map(std::vector & out, } -template -int -deepmd:: -session_get_scalar(Session*, const std::string, const std::string); +// template +// int +// deepmd:: +// session_get_scalar(Session*, const std::string, const std::string); -template -void -deepmd:: -session_get_vector(std::vector &, Session*, const std::string, const std::string); +// template +// void +// deepmd:: +// session_get_vector(std::vector &, Session*, const std::string, const std::string); template void @@ -595,15 +595,15 @@ select_map( const int & stride); -template -float -deepmd:: -session_get_scalar(Session*, const std::string, const std::string); +// template +// float +// deepmd:: +// session_get_scalar(Session*, const std::string, const std::string); -template -void -deepmd:: -session_get_vector(std::vector &, Session*, const std::string, const std::string); +// template +// void +// deepmd:: +// session_get_vector(std::vector &, Session*, const std::string, const std::string); template void @@ -615,15 +615,15 @@ select_map( const int & stride); -template -double -deepmd:: -session_get_scalar(Session*, const std::string, const std::string); +// template +// double +// deepmd:: +// session_get_scalar(Session*, const std::string, const std::string); -template -void -deepmd:: -session_get_vector(std::vector &, Session*, const std::string, const std::string); +// template +// void +// deepmd:: +// session_get_vector(std::vector &, Session*, const std::string, const std::string); template void @@ -635,15 +635,15 @@ select_map( const int & stride); -template -deepmd::STRINGTYPE -deepmd:: -session_get_scalar(Session*, const std::string, const std::string); +// template +// deepmd::STRINGTYPE +// deepmd:: +// session_get_scalar(Session*, const std::string, const std::string); -template -void -deepmd:: -session_get_vector(std::vector &, Session*, const std::string, const std::string); +// template +// void +// deepmd:: +// session_get_vector(std::vector &, Session*, const std::string, const std::string); template void diff --git a/source/api_cc/src/paddle_common.cc b/source/api_cc/src/paddle_common.cc new file mode 100755 index 0000000000..84d89224c3 --- /dev/null +++ b/source/api_cc/src/paddle_common.cc @@ -0,0 +1,194 @@ +#include "paddle_common.h" +#include "AtomMap.h" +#include "device.h" + +template +void SetInputs(const std::vector& param_vec, const std::vector param_shape, const std::string& input_name, + const std::shared_ptr& predictor) { + auto tensor = predictor->GetInputHandle(input_name); + tensor->Reshape(param_shape); + tensor->CopyFromCpu(param_vec.data()); +} + +int deepmd::get_math_lib_num_threads() +{ + int num_nthreads = 1; // By default use 1 thread + const char* str_num_nthreads = std::getenv("OMP_NUM_THREADS"); + if (str_num_nthreads && std::string(str_num_nthreads)!=std::string("") && atoi(str_num_nthreads) >= 0) { + num_nthreads = atoi(str_num_nthreads); + } + return num_nthreads; +} + +int deepmd::paddle_input_tensors ( + std::shared_ptr predictor, + const std::vector & dcoord_, + const int & ntypes, + const std::vector & datype_, + const std::vector & dbox, + const deepmd::VALUETYPE & cell_size, + const std::vector & fparam_, + const std::vector & aparam_, + const deepmd::AtomMap& atommap){ + + // Deal with input + bool b_pbc = (dbox.size() == 9); + + int nframes = 1; + int nall = dcoord_.size() / 3; + int nloc = nall; + assert (nall == datype_.size()); + + std::vector datype = atommap.get_type(); + std::vector type_count (ntypes, 0); + for (unsigned ii = 0; ii < datype.size(); ++ii){ + type_count[datype[ii]] ++; + } + datype.insert (datype.end(), datype_.begin() + nloc, datype_.end()); + + // Calculate params shape + std::vector coord_shape = {nframes, nall * 3}; + std::vector type_shape = {nframes, nall}; + std::vector natoms_shape = {2 + ntypes}; + std::vector box_shape = {nframes, 9}; + std::vector mesh_shape; + if (b_pbc==true){ + mesh_shape.push_back(6); + } else{ + mesh_shape.push_back(0); + } + assert (mesh_shape.size() == 1); + assert (natoms_shape.size() == 1); + std::vector fparam_shape = {nframes, static_cast(fparam_.size())}; + std::vector aparam_shape = {nframes, static_cast(aparam_.size())}; + + std::vector dcoord (dcoord_); + atommap.forward(dcoord.begin(), dcoord_.begin(), 3); + std::vector coord(nframes * nall * 3, 0.0); + std::vector box(nframes * 9, 0.0); + std::vector type(nframes * nall, 0), mesh(mesh_shape[0]), natoms(natoms_shape[0]); + + for (int i = 0; i < nframes; i++){ + for (int j = 0; j < dcoord.size(); j++){ + coord[i * nframes + j] = dcoord[j]; + } + + for (int j = 0; j < dbox.size(); j++){ + if (b_pbc){ + box[i * nframes + j] = dbox[j]; + }else{ + box[i * nframes + j] = 0.0; + } + } + + for (int j = 0; j < nall; j++){ + type[i * nframes + j] = datype[j]; + } + + // for (int jj = 0; jj < fparam_.size(); ++jj){ + // fparam(ii, jj) = fparam_[jj]; + // } + // for (int jj = 0; jj < aparam_.size(); ++jj){ + // aparam(ii, jj) = aparam_[jj]; + // } + } + + if (b_pbc){ + fill(mesh.begin(), mesh.end(), 0); + } + natoms[0] = nloc; + natoms[1] = nall; + for (int k = 0; k < ntypes; ++k) natoms[k+2] = type_count[k]; + + auto input_names = predictor->GetInputNames(); + SetInputs(coord, coord_shape, input_names[0], predictor); + SetInputs(type, type_shape, input_names[1], predictor); + SetInputs(natoms, natoms_shape, input_names[2], predictor); + SetInputs(box, box_shape, input_names[3], predictor); + SetInputs(mesh, mesh_shape, input_names[4], predictor); + return nloc; +} + +int deepmd::paddle_input_tensors ( + std::shared_ptr predictor, + const std::vector & dcoord_, + const int & ntypes, + const std::vector & datype_, + const std::vector & dbox, + deepmd::InputNlist & dlist, + const std::vector & fparam_, + const std::vector & aparam_, + const deepmd::AtomMap& atommap, + const int nghost, + const int ago) +{ + assert (dbox.size() == 9); + int nframes = 1; + int nall = dcoord_.size() / 3; + int nloc = nall - nghost; + assert (nall == datype_.size()); + + std::vector datype = atommap.get_type(); + std::vector type_count (ntypes, 0); + for (unsigned ii = 0; ii < datype.size(); ++ii){ + type_count[datype[ii]] ++; + } + datype.insert (datype.end(), datype_.begin() + nloc, datype_.end()); + // Calculate params shape + std::vector coord_shape = {nframes, nall * 3}; + std::vector type_shape = {nframes, nall}; + std::vector box_shape = {nframes, 9}; + std::vector mesh_shape = {16}; // mesh_shape.AddDim (16); + std::vector natoms_shape = {2 + ntypes}; + std::vector fparam_shape = {nframes, static_cast(fparam_.size())}; + std::vector aparam_shape = {nframes, static_cast(aparam_.size())}; + + std::vector dcoord (dcoord_); + atommap.forward (dcoord.begin(), dcoord_.begin(), 3); + + std::vector coord(nframes * nall * 3, 0.0), box(nframes * 9, 0.0); + std::vector type(nframes * nall, 0), mesh(mesh_shape[0], 0), natoms(natoms_shape[0], 0); + + for (int i = 0; i < nframes; i++){ + for (int j = 0; j < nall * 3; ++j){ + coord[i * nframes + j]= dcoord[j]; + } + for (int j = 0; j < 9; ++j){ + box[i * nframes + j] = dbox[j]; + } + for (int j = 0; j < nall; ++j){ + type[i * nframes + j] = datype[j]; + } + // for (int j = 0; j < fparam_.size(); ++j){ + // fparam[i * nframes + j] = fparam_[j]; + // } + // for (int j = 0; j < aparam_.size(); ++j){ + // aparam[i * nframes + j] = aparam_[j]; + // } + } + for (int i = 0; i < 16; ++i) mesh[i] = 0; + + const int stride = sizeof(int *) / sizeof(int); + assert (stride * sizeof(int) == sizeof(int *)); + assert (stride <= 4); + mesh[0] = ago; + mesh[1] = dlist.inum; + mesh[2] = 0; + mesh[3] = 0; + memcpy(&mesh[4], &(dlist.ilist), sizeof(int *)); + memcpy(&mesh[8], &(dlist.numneigh), sizeof(int *)); + memcpy(&mesh[12], &(dlist.firstneigh), sizeof(int **)); + + natoms[0] = nloc; + natoms[1] = nall; + for (int i = 0; i < ntypes; ++i) + natoms[i+2] = type_count[i]; + + auto input_names = predictor->GetInputNames(); + SetInputs(coord, coord_shape, input_names[0], predictor); + SetInputs(type, type_shape, input_names[1], predictor); + SetInputs(natoms, natoms_shape, input_names[2], predictor); + SetInputs(box, box_shape, input_names[3], predictor); + SetInputs(mesh, mesh_shape, input_names[4], predictor); + return nloc; +} diff --git a/source/cmake/FindFluid.cmake b/source/cmake/FindFluid.cmake new file mode 100755 index 0000000000..6f0a6fddfc --- /dev/null +++ b/source/cmake/FindFluid.cmake @@ -0,0 +1,154 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the pLicense at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License + +set(PADDLE_FOUND OFF) + +if(NOT PADDLE_ROOT) + set(PADDLE_ROOT $ENV{PADDLE_ROOT} CACHE PATH "Paddle Path") +endif() +if(NOT PADDLE_ROOT) + message(FATAL_ERROR "Set PADDLE_ROOT as your root directory installed PaddlePaddle") +endif() +set(THIRD_PARTY_ROOT ${PADDLE_ROOT}/third_party) + +if(USE_CUDA_TOOLKIT) + set(CUDA_ROOT $ENV{CUDA_ROOT} CACHE PATH "CUDA root Path") + set(CUDNN_ROOT $ENV{CUDNN_ROOT} CACHE PATH "CUDNN root Path") +endif() + +# Support directory orgnizations +find_path(PADDLE_INC_DIR NAMES paddle_inference_api.h PATHS ${PADDLE_ROOT}/paddle/include) +if(PADDLE_INC_DIR) + set(LIB_PATH "paddle/lib") +else() + find_path(PADDLE_INC_DIR NAMES paddle/fluid/inference/paddle_inference_api.h PATHS ${PADDLE_ROOT}) +# if(PADDLE_INC_DIR) +# include_directories(${PADDLE_ROOT}/paddle/fluid/inference) +# endif() + set(LIB_PATH "paddle/fluid/inference") +endif() + +include_directories(${PADDLE_ROOT}) + +find_library(PADDLE_FLUID_SHARED_LIB NAMES "libpaddle_inference.so" PATHS + ${PADDLE_ROOT}/${LIB_PATH}) +find_library(PADDLE_FLUID_STATIC_LIB NAMES "libpaddle_inference.a" PATHS + ${PADDLE_ROOT}/${LIB_PATH}) + +if(USE_SHARED AND PADDLE_INC_DIR AND PADDLE_FLUID_SHARED_LIB) + set(PADDLE_FOUND ON) + add_library(paddle_fluid_shared SHARED IMPORTED) + set_target_properties(paddle_fluid_shared PROPERTIES IMPORTED_LOCATION + ${PADDLE_FLUID_SHARED_LIB}) + set(PADDLE_LIBRARIES paddle_fluid_shared) + message(STATUS "Found PaddlePaddle Fluid (include: ${PADDLE_INC_DIR}; " + "library: ${PADDLE_FLUID_SHARED_LIB}") +elseif(PADDLE_INC_DIR AND PADDLE_FLUID_STATIC_LIB) + set(PADDLE_FOUND ON) + add_library(paddle_fluid_static STATIC IMPORTED) + set_target_properties(paddle_fluid_static PROPERTIES IMPORTED_LOCATION + ${PADDLE_FLUID_STATIC_LIB}) + set(PADDLE_LIBRARIES paddle_fluid_static) + message(STATUS "Found PaddlePaddle Fluid (include: ${PADDLE_INC_DIR}; " + "library: ${PADDLE_FLUID_STATIC_LIB}") +else() + set(PADDLE_FOUND OFF) + message(WARNING "Cannot find PaddlePaddle Fluid under ${PADDLE_ROOT}") + return() +endif() + +# including directory of third_party libraries +set(PADDLE_THIRD_PARTY_INC_DIRS) +function(third_party_include TARGET_NAME HEADER_NAME TARGET_DIRNAME) + find_path(PADDLE_${TARGET_NAME}_INC_DIR NAMES ${HEADER_NAME} PATHS + ${TARGET_DIRNAME} + NO_DEFAULT_PATH) + if(PADDLE_${TARGET_NAME}_INC_DIR) + message(STATUS "Found PaddlePaddle third_party including directory: " ${PADDLE_${TARGET_NAME}_INC_DIR}) + set(PADDLE_THIRD_PARTY_INC_DIRS ${PADDLE_THIRD_PARTY_INC_DIRS} ${PADDLE_${TARGET_NAME}_INC_DIR} PARENT_SCOPE) + endif() +endfunction() + +third_party_include(glog glog/logging.h ${THIRD_PARTY_ROOT}/install/glog/include) +third_party_include(protobuf google/protobuf/message.h ${THIRD_PARTY_ROOT}/install/protobuf/include) +third_party_include(gflags gflags/gflags.h ${THIRD_PARTY_ROOT}/install/gflags/include) +third_party_include(cryptopp cryptopp/cryptlib.h ${THIRD_PARTY_ROOT}/install/cryptopp/include) +third_party_include(utf8proc utf8proc/utf8proc.h ${THIRD_PARTY_ROOT}/install/utf8proc/include) +if(USE_CUDA_TOOLKIT) + third_party_include(cuda cuda.h ${CUDA_ROOT}/include) + third_party_include(cudnn cudnn.h ${CUDNN_ROOT}/include) +endif() + +message(STATUS "PaddlePaddle need to include these third party directories: ${PADDLE_THIRD_PARTY_INC_DIRS}") +include_directories(${PADDLE_THIRD_PARTY_INC_DIRS}) + +set(PADDLE_THIRD_PARTY_LIBRARIES) +function(third_party_library TARGET_NAME TARGET_DIRNAME) + set(library_names ${ARGN}) + set(local_third_party_libraries) + foreach(lib ${library_names}) + string(REGEX REPLACE "^lib" "" lib_noprefix ${lib}) + if(${lib} MATCHES "${CMAKE_STATIC_LIBRARY_SUFFIX}$") + set(libtype STATIC) + string(REGEX REPLACE "${CMAKE_STATIC_LIBRARY_SUFFIX}$" "" libname ${lib_noprefix}) + elseif(${lib} MATCHES "${CMAKE_SHARED_LIBRARY_SUFFIX}(\\.[0-9]+)?$") + set(libtype SHARED) + string(REGEX REPLACE "${CMAKE_SHARED_LIBRARY_SUFFIX}(\\.[0-9]+)?$" "" libname ${lib_noprefix}) + else() + message(FATAL_ERROR "Unknown library type: ${lib}") + endif() + find_library(${libname}_LIBRARY NAMES "${lib}" PATHS + ${TARGET_DIRNAME} + NO_DEFAULT_PATH) + if(${libname}_LIBRARY) + set(${TARGET_NAME}_FOUND ON PARENT_SCOPE) + add_library(${libname} ${libtype} IMPORTED) + set_target_properties(${libname} PROPERTIES IMPORTED_LOCATION ${${libname}_LIBRARY}) + set(local_third_party_libraries ${local_third_party_libraries} ${libname}) + message(STATUS "Found PaddlePaddle third_party library: " ${${libname}_LIBRARY}) + else() + set(${TARGET_NAME}_FOUND OFF PARENT_SCOPE) + message(WARNING "Cannot find ${lib} under ${THIRD_PARTY_ROOT}") + endif() + endforeach() + set(PADDLE_THIRD_PARTY_LIBRARIES ${PADDLE_THIRD_PARTY_LIBRARIES} ${local_third_party_libraries} PARENT_SCOPE) +endfunction() + +if(NOT BUILD_PY_IF) +set(OP_DIR "${PROJECT_SOURCE_DIR}/op/paddle_ops/srcs") +if(USE_CUDA_TOOLKIT) + file(GLOB CUSTOM_OPERATOR_FILES ${OP_DIR}/*.cc) +else() + file(GLOB CUSTOM_OPERATOR_FILES ${OP_DIR}/*_cpu.cc) +endif() +add_library(pd_infer_custom_op SHARED ${CUSTOM_OPERATOR_FILES}) +install(TARGETS pd_infer_custom_op DESTINATION lib/) +endif() + +third_party_library(mklml ${THIRD_PARTY_ROOT}/install/mklml/lib libiomp5.so libmklml_intel.so) +third_party_library(mkldnn ${THIRD_PARTY_ROOT}/install/mkldnn/lib libmkldnn.so.0) +if(NOT USE_SHARED) + third_party_library(glog ${THIRD_PARTY_ROOT}/install/glog/lib libglog.a) + third_party_library(protobuf ${THIRD_PARTY_ROOT}/install/protobuf/lib libprotobuf.a) + third_party_library(gflags ${THIRD_PARTY_ROOT}/install/gflags/lib libgflags.a) + third_party_library(cryptopp ${THIRD_PARTY_ROOT}/install/cryptopp/lib libcryptopp.a) + third_party_library(utf8proc ${THIRD_PARTY_ROOT}/install/utf8proc/lib libutf8proc.a) + if(NOT mklml_FOUND) + third_party_library(openblas ${THIRD_PARTY_ROOT}/install/openblas/lib libopenblas.a) + endif() + third_party_library(xxhash ${THIRD_PARTY_ROOT}/install/xxhash/lib libxxhash.a) + if(USE_CUDA_TOOLKIT) + third_party_library(cudart ${CUDA_ROOT}/lib64 libcudart.so) + endif() +endif() diff --git a/source/cmake/cmake_lammps.cmake.in b/source/cmake/cmake_lammps.cmake.in index bee237a1c7..38eae61425 100644 --- a/source/cmake/cmake_lammps.cmake.in +++ b/source/cmake/cmake_lammps.cmake.in @@ -20,4 +20,4 @@ file ( INSTALL DESTINATION "${LMP_INSTALL_PREFIX}" TYPE FILE FILES "@CMAKE_BINARY_DIR@/lmp/pair_deepmd.h" -) +) \ No newline at end of file diff --git a/source/cmake/googletest.cmake.in b/source/cmake/googletest.cmake.in old mode 100644 new mode 100755 index c6247af53c..37b622bf30 --- a/source/cmake/googletest.cmake.in +++ b/source/cmake/googletest.cmake.in @@ -5,7 +5,7 @@ project(googletest-download NONE) include(ExternalProject) ExternalProject_Add(googletest GIT_REPOSITORY https://github.com/google/googletest.git - GIT_TAG master + GIT_TAG main SOURCE_DIR "${CMAKE_CURRENT_BINARY_DIR}/googletest-src" BINARY_DIR "${CMAKE_CURRENT_BINARY_DIR}/googletest-build" CONFIGURE_COMMAND "" diff --git a/source/lmp/CMakeLists.txt b/source/lmp/CMakeLists.txt old mode 100644 new mode 100755 index bc52c8fa81..b4f6248f4e --- a/source/lmp/CMakeLists.txt +++ b/source/lmp/CMakeLists.txt @@ -10,3 +10,4 @@ list (APPEND LMP_INSTALL_FILES ${LMP_SHSCRIPT}) configure_file("env.sh.in" "env.sh" @ONLY) configure_file("pair_deepmd.h.in" "pair_deepmd.h" @ONLY) configure_file("lammps_install_list.txt.in" "lammps_install_list.txt" @ONLY) + diff --git a/source/lmp/Install.sh b/source/lmp/Install.sh old mode 100644 new mode 100755 diff --git a/source/lmp/deepmd_fix_ttm.patch b/source/lmp/deepmd_fix_ttm.patch old mode 100644 new mode 100755 diff --git a/source/lmp/env.sh.in b/source/lmp/env.sh.in old mode 100644 new mode 100755 index a59ad84960..f614296b6c --- a/source/lmp/env.sh.in +++ b/source/lmp/env.sh.in @@ -1,11 +1,5 @@ DEEPMD_ROOT=@CMAKE_INSTALL_PREFIX@ -TENSORFLOW_INCLUDE_DIRS="@TensorFlow_INCLUDE_DIRS@" -TENSORFLOW_LIBRARY_PATH="@TensorFlow_LIBRARY_PATH@" -TF_INCLUDE_DIRS=`echo $TENSORFLOW_INCLUDE_DIRS | sed "s/;/ -I/g"` -TF_LIBRARY_PATH=`echo $TENSORFLOW_LIBRARY_PATH | sed "s/;/ -L/g"` -TF_RPATH=`echo $TENSORFLOW_LIBRARY_PATH | sed "s/;/ -Wl,-rpath=/g"` - -NNP_INC=" -std=c++11 @PREC_DEF@ @TTM_DEF@ @OLD_LMP_PPPM_DEF@ -I$TF_INCLUDE_DIRS -I$DEEPMD_ROOT/include/ " -NNP_PATH=" -L$TF_LIBRARY_PATH -L$DEEPMD_ROOT/lib" -NNP_LIB=" -Wl,--no-as-needed -l@LIB_DEEPMD_OP_CUDA@ -l@LIB_DEEPMD_OP@ -l@LIB_DEEPMD_CC@ -l@LIB_DEEPMD@ -ltensorflow_cc -ltensorflow_framework -Wl,-rpath=$TF_RPATH -Wl,-rpath=$DEEPMD_ROOT/lib" +NNP_INC=" -std=c++14 @PREC_DEF@ @TTM_DEF@ @OLD_LMP_PPPM_DEF@ -I$DEEPMD_ROOT/include -I$paddle_root" +NNP_PATH=" -L$DEEPMD_ROOT/lib" +NNP_LIB=" -Wl,--no-as-needed -l@LIB_DEEPMD_CC@ -l@LIB_DEEPMD@ -Wl,-rpath=$DEEPMD_ROOT/lib -lpd_infer_custom_op" diff --git a/source/lmp/fix_dplr.cpp b/source/lmp/fix_dplr.cpp deleted file mode 100644 index 5ce85490fc..0000000000 --- a/source/lmp/fix_dplr.cpp +++ /dev/null @@ -1,538 +0,0 @@ -#include -#include -#include -#include "atom.h" -#include "domain.h" -#include "comm.h" -#include "force.h" -#include "update.h" -#include "error.h" -#include "neighbor.h" -#include "neigh_list.h" -#include "fix.h" -#include "fix_dplr.h" -#include "pppm_dplr.h" - -using namespace LAMMPS_NS; -using namespace FixConst; -using namespace std; - -static bool -is_key (const string& input) -{ - vector keys ; - keys.push_back("model"); - keys.push_back("type_associate"); - keys.push_back("bond_type"); - keys.push_back("efield"); - for (int ii = 0; ii < keys.size(); ++ii){ - if (input == keys[ii]) { - return true; - } - } - return false; -} - - -FixDPLR::FixDPLR(LAMMPS *lmp, int narg, char **arg) - :Fix(lmp, narg, arg), - efield(3, 0.0), - efield_fsum(4, 0.0), - efield_fsum_all(4, 0.0), - efield_force_flag(0) -{ - virial_flag = 1; - - if (strcmp(update->unit_style,"metal") != 0) { - error->all(FLERR,"Pair deepmd requires metal unit, please set it by \"units metal\""); - } - - int iarg = 3; - vector map_vec; - bond_type.clear(); - while (iarg < narg) { - if (! is_key(arg[iarg])) { - error->all(FLERR,"Illegal pair_style command\nwrong number of parameters\n"); - } - if (string(arg[iarg]) == string("model")) { - if (iarg+1 > narg) error->all(FLERR,"Illegal fix adapt command"); - model = string(arg[iarg+1]); - iarg += 2; - } - else if (string(arg[iarg]) == string("efield")) { - if (iarg+3 > narg) error->all(FLERR,"Illegal fix adapt command, efield should be provided 3 float numbers"); - efield[0] = atof(arg[iarg+1]); - efield[1] = atof(arg[iarg+2]); - efield[2] = atof(arg[iarg+3]); - iarg += 4; - } - else if (string(arg[iarg]) == string("type_associate")) { - int iend = iarg+1; - while (iend < narg && (! is_key(arg[iend]) )) { - map_vec.push_back(atoi(arg[iend])-1); - iend ++; - } - iarg = iend; - } - else if (string(arg[iarg]) == string("bond_type")) { - int iend = iarg+1; - while (iend < narg && (! is_key(arg[iend]) )) { - bond_type.push_back(atoi(arg[iend])-1); - iend ++; - } - sort(bond_type.begin(), bond_type.end()); - iarg = iend; - } - else { - break; - } - } - assert(map_vec.size() % 2 == 0), "number of ints provided by type_associate should be even"; - for (int ii = 0; ii < map_vec.size()/2; ++ii){ - type_asso[map_vec[ii*2+0]] = map_vec[ii*2+1]; - bk_type_asso[map_vec[ii*2+1]] = map_vec[ii*2+0]; - } - - // dpt.init(model); - // dtm.init("frozen_model.pb"); - dpt.init(model, 0, "dipole_charge"); - dtm.init(model, 0, "dipole_charge"); - - sel_type = dpt.sel_types(); - sort(sel_type.begin(), sel_type.end()); - dpl_type.clear(); - for (int ii = 0; ii < sel_type.size(); ++ii){ - dpl_type.push_back(type_asso[sel_type[ii]]); - } - - pair_deepmd = (PairDeepMD *) force->pair_match("deepmd",1); - if (!pair_deepmd) { - error->all(FLERR,"pair_style deepmd should be set before this fix\n"); - } - - // set comm size needed by this fix - comm_reverse = 3; -} - -int FixDPLR::setmask() -{ - int mask = 0; - mask |= THERMO_ENERGY; - mask |= POST_INTEGRATE; - mask |= PRE_FORCE; - mask |= POST_FORCE; - return mask; -} - -void FixDPLR::init() -{ - // double **xx = atom->x; - // double **vv = atom->v; - // int nlocal = atom->nlocal; - // for (int ii = 0; ii < nlocal; ++ii){ - // cout << xx[ii][0] << " " - // << xx[ii][1] << " " - // << xx[ii][2] << " " - // << vv[ii][0] << " " - // << vv[ii][1] << " " - // << vv[ii][2] << " " - // << endl; - // } -} - -void FixDPLR::setup(int vflag) -{ - // if (strstr(update->integrate_style,"verlet")) - // post_force(vflag); - // else { - // error->all(FLERR, "respa is not supported by this fix"); - // } - if (vflag) { - v_setup(vflag); - } - else { - evflag = 0; - } -} - - -void -FixDPLR::get_valid_pairs(vector >& pairs) -{ - pairs.clear(); - - int nlocal = atom->nlocal; - int nghost = atom->nghost; - int nall = nlocal + nghost; - vector dtype (nall); - // get type - { - int *type = atom->type; - for (int ii = 0; ii < nall; ++ii){ - dtype[ii] = type[ii] - 1; - } - } - - int **bondlist = neighbor->bondlist; - int nbondlist = neighbor->nbondlist; - for (int ii = 0; ii < nbondlist; ++ii){ - int idx0=-1, idx1=-1; - int bd_type = bondlist[ii][2] - 1; - if ( ! binary_search(bond_type.begin(), bond_type.end(), bd_type) ){ - continue; - } - if (binary_search(sel_type.begin(), sel_type.end(), dtype[bondlist[ii][0]]) - && - binary_search(dpl_type.begin(), dpl_type.end(), dtype[bondlist[ii][1]]) - ){ - idx0 = bondlist[ii][0]; - idx1 = bondlist[ii][1]; - } - else if (binary_search(sel_type.begin(), sel_type.end(), dtype[bondlist[ii][1]]) - && - binary_search(dpl_type.begin(), dpl_type.end(), dtype[bondlist[ii][0]]) - ){ - idx0 = bondlist[ii][1]; - idx1 = bondlist[ii][0]; - } - else { - error->all(FLERR, "find a bonded pair the types of which are not associated"); - } - if ( ! (idx0 < nlocal && idx1 < nlocal) ){ - error->all(FLERR, "find a bonded pair that is not on the same processor, something should not happen"); - } - pairs.push_back(pair(idx0, idx1)); - } -} - -void FixDPLR::post_integrate() -{ - double **x = atom->x; - double **v = atom->v; - int *type = atom->type; - int nlocal = atom->nlocal; - int nghost = atom->nghost; - int nall = nlocal + nghost; - - vector > valid_pairs; - get_valid_pairs(valid_pairs); - - for (int ii = 0; ii < valid_pairs.size(); ++ii){ - int idx0 = valid_pairs[ii].first; - int idx1 = valid_pairs[ii].second; - for (int dd = 0; dd < 3; ++dd){ - x[idx1][dd] = x[idx0][dd] ; - v[idx1][dd] = v[idx0][dd] ; - // v[idx1][dd] = 0.0; - } - } -} - -void FixDPLR::pre_force(int vflag) -{ - double **x = atom->x; - int *type = atom->type; - int nlocal = atom->nlocal; - int nghost = atom->nghost; - int nall = nlocal + nghost; - - // if (eflag_atom) { - // error->all(FLERR,"atomic energy calculation is not supported by this fix\n"); - // } - - // declear inputs - vector dtype (nall); - vector dbox (9, 0) ; - vector dcoord (nall * 3, 0.); - // get type - for (int ii = 0; ii < nall; ++ii){ - dtype[ii] = type[ii] - 1; - } - // get box - dbox[0] = domain->h[0]; // xx - dbox[4] = domain->h[1]; // yy - dbox[8] = domain->h[2]; // zz - dbox[7] = domain->h[3]; // zy - dbox[6] = domain->h[4]; // zx - dbox[3] = domain->h[5]; // yx - // get coord - for (int ii = 0; ii < nall; ++ii){ - for (int dd = 0; dd < 3; ++dd){ - dcoord[ii*3+dd] = x[ii][dd] - domain->boxlo[dd]; - } - } - // get lammps nlist - NeighList * list = pair_deepmd->list; - deepmd::InputNlist lmp_list (list->inum, list->ilist, list->numneigh, list->firstneigh); - // declear output - vector tensor; - // compute - dpt.compute(tensor, dcoord, dtype, dbox, nghost, lmp_list); - // cout << "tensor of size " << tensor.size() << endl; - // cout << "nghost " << nghost << endl; - // cout << "nall " << dtype.size() << endl; - // cout << "nloc " << nlocal << endl; - // for (int ii = 0; ii < tensor.size(); ++ii){ - // if (ii%3 == 0){ - // cout << endl; - // } - // cout << tensor[ii] << "\t"; - // } - // cout << endl; - // for (int ii = 0; ii < nlocal * 3; ++ii){ - // if (ii%3 == 0){ - // cout << endl; - // } - // cout << dcoord[ii] << "\t"; - // } - // int max_type = 0; - // for (int ii = 0; ii < dtype.size(); ++ii){ - // if (dtype[ii] > max_type) { - // max_type = dtype[ii]; - // } - // } - - // selected type - vector dpl_type; - for (int ii = 0; ii < sel_type.size(); ++ii){ - dpl_type.push_back(type_asso[sel_type[ii]]); - } - vector sel_fwd, sel_bwd; - int sel_nghost; - deepmd::select_by_type(sel_fwd, sel_bwd, sel_nghost, dcoord, dtype, nghost, sel_type); - int sel_nall = sel_bwd.size(); - int sel_nloc = sel_nall - sel_nghost; - vector sel_type(sel_bwd.size()); - deepmd::select_map(sel_type, dtype, sel_fwd, 1); - - deepmd::AtomMap atom_map(sel_type.begin(), sel_type.begin() + sel_nloc); - const vector & sort_fwd_map(atom_map.get_fwd_map()); - - vector > valid_pairs; - get_valid_pairs(valid_pairs); - - int odim = dpt.output_dim(); - assert(odim == 3); - dipole_recd.resize(nall * 3); - fill(dipole_recd.begin(), dipole_recd.end(), 0.0); - for (int ii = 0; ii < valid_pairs.size(); ++ii){ - int idx0 = valid_pairs[ii].first; - int idx1 = valid_pairs[ii].second; - assert(idx0 < sel_fwd.size() && sel_fwd[idx0] < sort_fwd_map.size()); - int res_idx = sort_fwd_map[sel_fwd[idx0]]; - // int ret_idx = dpl_bwd[res_idx]; - for (int dd = 0; dd < 3; ++dd){ - x[idx1][dd] = x[idx0][dd] + tensor[res_idx * 3 + dd]; - // res_buff[idx1 * odim + dd] = tensor[res_idx * odim + dd]; - dipole_recd[idx0*3+dd] = tensor[res_idx * 3 + dd]; - } - } - // cout << "-------------------- fix/dplr: pre force " << endl; - // for (int ii = 0; ii < nlocal; ++ii){ - // cout << ii << " "; - // for (int dd = 0; dd < 3; ++dd){ - // cout << x[ii][dd] << " " ; - // } - // cout << endl; - // } -} - - -void FixDPLR::post_force(int vflag) -{ - if (vflag) { - v_setup(vflag); - } - else { - evflag = 0; - } - if (vflag_atom) { - error->all(FLERR,"atomic virial calculation is not supported by this fix\n"); - } - - PPPMDPLR * pppm_dplr = (PPPMDPLR*) force->kspace_match("pppm/dplr", 1); - if (!pppm_dplr) { - error->all(FLERR,"kspace_style pppm/dplr should be set before this fix\n"); - } - const vector & dfele_(pppm_dplr->get_fele()); - int nlocal = atom->nlocal; - int nghost = atom->nghost; - int nall = nlocal + nghost; - vector dcoord(nall*3, 0.0), dbox(9, 0.0), dfele(nlocal*3, 0.0); - vector dtype(nall, 0); - // set values for dcoord, dbox, dfele - { - int *type = atom->type; - for (int ii = 0; ii < nall; ++ii){ - dtype[ii] = type[ii] - 1; - } - dbox[0] = domain->h[0]; // xx - dbox[4] = domain->h[1]; // yy - dbox[8] = domain->h[2]; // zz - dbox[7] = domain->h[3]; // zy - dbox[6] = domain->h[4]; // zx - dbox[3] = domain->h[5]; // yx - // get coord - double ** x = atom->x; - for (int ii = 0; ii < nall; ++ii){ - for (int dd = 0; dd < 3; ++dd){ - dcoord[ii*3+dd] = x[ii][dd] - domain->boxlo[dd]; - } - } - assert(dfele_.size() == nlocal * 3); - // revise force according to efield - for (int ii = 0; ii < nlocal*3; ++ii){ - dfele[ii] = dfele_[ii]; - } - // revise force and virial according to efield - double * q = atom->q; - imageint *image = atom->image; - double unwrap[3]; - double v[6]; - efield_fsum[0] = efield_fsum[1] = efield_fsum[2] = efield_fsum[3] = 0.0; - efield_force_flag = 0; - for (int ii = 0; ii < nlocal; ++ii){ - double tmpf[3]; - for (int dd = 0; dd < 3; ++dd){ - tmpf[dd] = q[ii] * efield[dd]; - } - for (int dd = 0; dd < 3; ++dd){ - dfele[ii*3+dd] += tmpf[dd]; - } - domain->unmap(x[ii],image[ii],unwrap); - efield_fsum[0] -= tmpf[0]*unwrap[0]+tmpf[1]*unwrap[1]+tmpf[2]*unwrap[2]; - efield_fsum[1] += tmpf[0]; - efield_fsum[2] += tmpf[1]; - efield_fsum[3] += tmpf[2]; - if (evflag) { - v[0] = tmpf[0] *unwrap[0]; - v[1] = tmpf[1] *unwrap[1]; - v[2] = tmpf[2] *unwrap[2]; - v[3] = tmpf[0] *unwrap[1]; - v[4] = tmpf[0] *unwrap[2]; - v[5] = tmpf[1] *unwrap[2]; - v_tally(ii, v); - } - } - } - // lmp nlist - NeighList * list = pair_deepmd->list; - deepmd::InputNlist lmp_list (list->inum, list->ilist, list->numneigh, list->firstneigh); - // bonded pairs - vector > valid_pairs; - get_valid_pairs(valid_pairs); - // output vects - vector dfcorr, dvcorr; - // compute - dtm.compute(dfcorr, dvcorr, dcoord, dtype, dbox, valid_pairs, dfele, nghost, lmp_list); - assert(dfcorr.size() == dcoord.size()); - assert(dfcorr.size() == nall * 3); - // backward communication of fcorr - dfcorr_buff.resize(dfcorr.size()); - copy(dfcorr.begin(), dfcorr.end(), dfcorr_buff.begin()); - comm->reverse_comm_fix(this,3); - copy(dfcorr_buff.begin(), dfcorr_buff.end(), dfcorr.begin()); - // // check and print - // cout << "-------------------- fix/dplr: post force " << endl; - // cout << "dfcorr.size() " << dfcorr.size() << endl; - // cout << "dcoord.size() " << dcoord.size() << endl; - // for (int ii = 0; ii < nlocal; ++ii){ - // cout << ii << "\t x: "; - // for (int dd = 0; dd < 3; ++dd){ - // cout << dcoord[ii*3+dd] << " \t " ; - // } - // cout << ii << "\t f: "; - // for (int dd = 0; dd < 3; ++dd){ - // cout << dfcorr[ii*3+dd] << " \t " ; - // } - // cout << endl; - // } - // apply the force correction - double ** f = atom->f; - for (int ii = 0; ii < nlocal; ++ii){ - for(int dd = 0; dd < 3; ++dd){ - f[ii][dd] += dfcorr[ii*3+dd]; - } - } - // cout << "virial corr1 "; - // for (int ii = 0; ii < 9; ++ii){ - // cout << dvcorr[ii] << " " ; - // } - // cout << endl; - for (int ii = 0; ii < valid_pairs.size(); ++ii){ - int idx0 = valid_pairs[ii].first; - int idx1 = valid_pairs[ii].second; - for (int dd0 = 0; dd0 < 3; ++dd0){ - for (int dd1 = 0; dd1 < 3; ++dd1){ - dvcorr[dd0*3+dd1] -= dfele[idx1*3+dd0] * dipole_recd[idx0*3+dd1]; - } - } - } - // cout << "virial corr2 "; - // for (int ii = 0; ii < 9; ++ii){ - // cout << dvcorr[ii] << " " ; - // } - // cout << endl; - if (evflag){ - double vv[6] = {0.0}; - vv[0] += dvcorr[0]; - vv[1] += dvcorr[4]; - vv[2] += dvcorr[8]; - vv[3] += dvcorr[3]; - vv[4] += dvcorr[6]; - vv[5] += dvcorr[7]; - v_tally(0, vv); - } -} - - -int FixDPLR::pack_reverse_comm(int n, int first, double *buf) -{ - int m = 0; - int last = first + n; - for (int i = first; i < last; i++) { - buf[m++] = dfcorr_buff[3*i+0]; - buf[m++] = dfcorr_buff[3*i+1]; - buf[m++] = dfcorr_buff[3*i+2]; - } - return m; -} - -/* ---------------------------------------------------------------------- */ - -void FixDPLR::unpack_reverse_comm(int n, int *list, double *buf) -{ - int m = 0; - for (int i = 0; i < n; i++) { - int j = list[i]; - dfcorr_buff[3*j+0] += buf[m++]; - dfcorr_buff[3*j+1] += buf[m++]; - dfcorr_buff[3*j+2] += buf[m++]; - } -} - -/* ---------------------------------------------------------------------- - return energy added by fix -------------------------------------------------------------------------- */ - -double FixDPLR::compute_scalar(void) -{ - if (efield_force_flag == 0) { - MPI_Allreduce(&efield_fsum[0],&efield_fsum_all[0],4,MPI_DOUBLE,MPI_SUM,world); - efield_force_flag = 1; - } - return efield_fsum_all[0]; -} - -/* ---------------------------------------------------------------------- - return total extra force due to fix -------------------------------------------------------------------------- */ - -double FixDPLR::compute_vector(int n) -{ - if (efield_force_flag == 0) { - MPI_Allreduce(&efield_fsum[0],&efield_fsum_all[0],4,MPI_DOUBLE,MPI_SUM,world); - efield_force_flag = 1; - } - return efield_fsum_all[n+1]; -} diff --git a/source/lmp/fix_dplr.h b/source/lmp/fix_dplr.h deleted file mode 100644 index 6de055529b..0000000000 --- a/source/lmp/fix_dplr.h +++ /dev/null @@ -1,58 +0,0 @@ -#ifdef FIX_CLASS - -FixStyle(dplr,FixDPLR) - -#else - -#ifndef LMP_FIX_DPLR_H -#define LMP_FIX_DPLR_H - -#include -#include "fix.h" -#include "pair_deepmd.h" -#include "deepmd/DeepTensor.h" -#include "deepmd/DataModifier.h" - -#ifdef HIGH_PREC -#define FLOAT_PREC double -#else -#define FLOAT_PREC float -#endif - -namespace LAMMPS_NS { - class FixDPLR : public Fix { -public: - FixDPLR(class LAMMPS *, int, char **); - virtual ~FixDPLR() {}; - int setmask(); - void init(); - void setup(int); - void post_integrate(); - void pre_force(int); - void post_force(int); - int pack_reverse_comm(int, int, double *); - void unpack_reverse_comm(int, int *, double *); - double compute_scalar(void); - double compute_vector(int); -private: - PairDeepMD * pair_deepmd; - deepmd::DeepTensor dpt; - deepmd::DipoleChargeModifier dtm; - std::string model; - int ntypes; - std::vector sel_type; - std::vector dpl_type; - std::vector bond_type; - std::map type_asso; - std::map bk_type_asso; - std::vector dipole_recd; - std::vector dfcorr_buff; - std::vector efield; - std::vector efield_fsum, efield_fsum_all; - int efield_force_flag; - void get_valid_pairs(std::vector >& pairs); - }; -} - -#endif // LMP_FIX_DPLR_H -#endif // FIX_CLASS diff --git a/source/lmp/lammps_install_list.txt.in b/source/lmp/lammps_install_list.txt.in old mode 100644 new mode 100755 diff --git a/source/lmp/pair_deepmd.cpp b/source/lmp/pair_deepmd.cpp old mode 100644 new mode 100755 index 8e2d691562..12b1e0f953 --- a/source/lmp/pair_deepmd.cpp +++ b/source/lmp/pair_deepmd.cpp @@ -1,3 +1,5 @@ +#include +#include #include #include #include @@ -27,6 +29,7 @@ using namespace std; static const char cite_user_deepmd_package[] = "USER-DEEPMD package:\n\n" + "based on PaddlePaddle framework and inference" "@article{Wang_ComputPhysCommun_2018_v228_p178,\n" " author = {Wang, Han and Zhang, Linfeng and Han, Jiequn and E, Weinan},\n" " doi = {10.1016/j.cpc.2018.03.016},\n" @@ -58,60 +61,43 @@ static int stringCmp(const void *a, const void* b) return sum; } -int PairDeepMD::get_node_rank() { - char host_name[MPI_MAX_PROCESSOR_NAME]; - memset(host_name, '\0', sizeof(char) * MPI_MAX_PROCESSOR_NAME); - char (*host_names)[MPI_MAX_PROCESSOR_NAME]; - int n, namelen, color, rank, nprocs, myrank; - size_t bytes; - MPI_Comm nodeComm; - - MPI_Comm_rank(MPI_COMM_WORLD, &rank); - MPI_Comm_size(MPI_COMM_WORLD, &nprocs); - MPI_Get_processor_name(host_name,&namelen); - - bytes = nprocs * sizeof(char[MPI_MAX_PROCESSOR_NAME]); - host_names = (char (*)[MPI_MAX_PROCESSOR_NAME]) malloc(bytes); - for (int ii = 0; ii < nprocs; ii++) { - memset(host_names[ii], '\0', sizeof(char) * MPI_MAX_PROCESSOR_NAME); - } - - strcpy(host_names[rank], host_name); - - for (n=0; n(input_file), std::istreambuf_iterator()); +} - MPI_Barrier(MPI_COMM_WORLD); - int looprank=myrank; - // printf (" Assigning device %d to process on node %s rank %d, OK\n",looprank, host_name, rank ); - free(host_names); - return looprank; +std::string PairDeepMD::get_prog_file_content(const std::string & model) { + int myrank = 0, root = 0; + MPI_Comm_rank(MPI_COMM_WORLD, &myrank); + int nchar = 0; + std::string file_content; + if (myrank == root) { + file_content = readfile(model); + nchar = file_content.size(); + } + MPI_Bcast(&nchar, 1, MPI_INT, root, MPI_COMM_WORLD); + char * buff = (char *)malloc(sizeof(char) * nchar); + if (myrank == root) { + memcpy(buff, file_content.c_str(), sizeof(char) * nchar); + } + MPI_Bcast(buff, nchar, MPI_CHAR, root, MPI_COMM_WORLD); + file_content.resize(nchar); + for (unsigned ii = 0; ii < nchar; ++ii) { + file_content[ii] = buff[ii]; + } + free(buff); + return file_content; } -std::string PairDeepMD::get_file_content(const std::string & model) { +std::string PairDeepMD::get_params_file_content(const std::string & model) { int myrank = 0, root = 0; MPI_Comm_rank(MPI_COMM_WORLD, &myrank); int nchar = 0; std::string file_content; if (myrank == root) { - deepmd::check_status(tensorflow::ReadFileToString(tensorflow::Env::Default(), model, &file_content)); + file_content = readfile(model); nchar = file_content.size(); } MPI_Bcast(&nchar, 1, MPI_INT, root, MPI_COMM_WORLD); @@ -128,10 +114,18 @@ std::string PairDeepMD::get_file_content(const std::string & model) { return file_content; } -std::vector PairDeepMD::get_file_content(const std::vector & models) { +std::vector PairDeepMD::get_prog_file_content(const std::vector & models) { std::vector file_contents(models.size()); for (unsigned ii = 0; ii < models.size(); ++ii) { - file_contents[ii] = get_file_content(models[ii]); + file_contents[ii] = get_prog_file_content(models[ii]); + } + return file_contents; +} + +std::vector PairDeepMD::get_params_file_content(const std::vector & models) { + std::vector file_contents(models.size()); + for (unsigned ii = 0; ii < models.size(); ++ii) { + file_contents[ii] = get_params_file_content(models[ii]); } return file_contents; } @@ -211,9 +205,9 @@ void PairDeepMD::make_ttm_aparam( // loop over atoms to assign aparam for (int ii = 0; ii < nlocal; ii++) { if (mask[ii] & ttm_fix->groupbit) { - double xscale = (x[ii][0] - domain->boxlo[0])/domain->xprd; - double yscale = (x[ii][1] - domain->boxlo[1])/domain->yprd; - double zscale = (x[ii][2] - domain->boxlo[2])/domain->zprd; + double xscale = static_cast((x[ii][0] - domain->boxlo[0])/domain->xprd); + double yscale = static_cast((x[ii][1] - domain->boxlo[1])/domain->yprd); + double zscale = static_cast((x[ii][2] - domain->boxlo[2])/domain->zprd); int ixnode = static_cast(xscale*nxnodes); int iynode = static_cast(yscale*nynodes); int iznode = static_cast(zscale*nznodes); @@ -263,7 +257,7 @@ PairDeepMD::print_summary(const string pre) const { if (comm->me == 0){ cout << "Summary of lammps deepmd module ..." << endl; - cout << pre << ">>> Info of deepmd-kit:" << endl; + cout << pre << ">>> Info of deepmd-kit based on Paddle:" << endl; deep_pot.print_summary(pre); cout << pre << ">>> Info of lammps module:" << endl; cout << pre << "use deepmd-kit at: " << STR_DEEPMD_ROOT << endl; @@ -272,12 +266,9 @@ PairDeepMD::print_summary(const string pre) const cout << pre << "source commit: " << STR_GIT_HASH << endl; cout << pre << "source commit at: " << STR_GIT_DATE << endl; cout << pre << "build float prec: " << STR_FLOAT_PREC << endl; - cout << pre << "build with tf inc: " << STR_TensorFlow_INCLUDE_DIRS << endl; - cout << pre << "build with tf lib: " << STR_TensorFlow_LIBRARY << endl; } } - PairDeepMD::~PairDeepMD() { if (allocated) { @@ -424,7 +415,7 @@ void PairDeepMD::compute(int eflag, int vflag) vector all_energy; vector> all_virial; vector> all_atom_energy; - vector> all_atom_virial; + vector> all_atom_virial; deep_pot_model_devi.compute(all_energy, all_force, all_virial, all_atom_energy, all_atom_virial, dcoord, dtype, dbox, nghost, lmp_list, ago, fparam, daparam); // deep_pot_model_devi.compute_avg (dener, all_energy); // deep_pot_model_devi.compute_avg (dforce, all_force); @@ -674,7 +665,8 @@ void PairDeepMD::settings(int narg, char **arg) { if (narg <= 0) error->all(FLERR,"Illegal pair_style command"); - vector models; + vector models_prog; + vector models_params; int iarg = 0; while (iarg < narg){ if (is_key(arg[iarg])) { @@ -683,19 +675,24 @@ void PairDeepMD::settings(int narg, char **arg) iarg ++; } for (int ii = 0; ii < iarg; ++ii){ - models.push_back(arg[ii]); + models_prog.push_back(arg[ii]); + models_params.push_back(arg[++ii]); } - numb_models = models.size(); + numb_models = models_prog.size(); if (numb_models == 1) { - deep_pot.init (arg[0], get_node_rank(), get_file_content(arg[0])); + auto prog_model = get_prog_file_content(arg[0]); + auto prog_params = get_params_file_content(arg[1]); + deep_pot.init (prog_model, prog_params); + // deep_pot.init (models_prog[0], models_params[0]); cutoff = deep_pot.cutoff (); numb_types = deep_pot.numb_types(); dim_fparam = deep_pot.dim_fparam(); dim_aparam = deep_pot.dim_aparam(); } else { - deep_pot.init (arg[0], get_node_rank(), get_file_content(arg[0])); - deep_pot_model_devi.init(models, get_node_rank(), get_file_content(models)); + auto progs_model = get_prog_file_content(models_prog); + auto progs_params = get_params_file_content(models_params); + deep_pot_model_devi.init(progs_model, progs_params); cutoff = deep_pot_model_devi.cutoff(); numb_types = deep_pot_model_devi.numb_types(); dim_fparam = deep_pot_model_devi.dim_fparam(); @@ -803,8 +800,8 @@ void PairDeepMD::settings(int narg, char **arg) cout << arg[0] << " "; } else { - for (int ii = 0; ii < models.size(); ++ii){ - cout << models[ii] << " "; + for (int ii = 0; ii < models_prog.size(); ++ii){ + cout << models_prog[ii] << " "; } } cout << endl diff --git a/source/lmp/pair_deepmd.h.in b/source/lmp/pair_deepmd.h.in old mode 100644 new mode 100755 index ee20de13c6..c5afb3de06 --- a/source/lmp/pair_deepmd.h.in +++ b/source/lmp/pair_deepmd.h.in @@ -8,7 +8,7 @@ PairStyle(deepmd,PairDeepMD) #define LMP_PAIR_NNP_H #include "pair.h" -#include "deepmd/DeepPot.h" +#include "deepmd/PaddleDeepPot.h" #include #include @@ -17,13 +17,11 @@ PairStyle(deepmd,PairDeepMD) #define GIT_BRANCH @GIT_BRANCH@ #define GIT_DATE @GIT_DATE@ #ifdef HIGH_PREC -#define FLOAT_PREC double +#define FLOAT_PREC float #else #define FLOAT_PREC float #endif #define DEEPMD_ROOT @CMAKE_INSTALL_PREFIX@ -#define TensorFlow_INCLUDE_DIRS @TensorFlow_INCLUDE_DIRS@ -#define TensorFlow_LIBRARY @TensorFlow_LIBRARY@ #define DPMD_CVT_STR(x) #x #define DPMD_CVT_ASSTR(X) DPMD_CVT_STR(X) #define STR_GIT_SUMM DPMD_CVT_ASSTR(GIT_SUMM) @@ -32,8 +30,6 @@ PairStyle(deepmd,PairDeepMD) #define STR_GIT_DATE DPMD_CVT_ASSTR(GIT_DATE) #define STR_FLOAT_PREC DPMD_CVT_ASSTR(FLOAT_PREC) #define STR_DEEPMD_ROOT DPMD_CVT_ASSTR(DEEPMD_ROOT) -#define STR_TensorFlow_INCLUDE_DIRS DPMD_CVT_ASSTR(TensorFlow_INCLUDE_DIRS) -#define STR_TensorFlow_LIBRARY DPMD_CVT_ASSTR(TensorFlow_LIBRARY) namespace LAMMPS_NS { @@ -51,19 +47,21 @@ class PairDeepMD : public Pair { void unpack_reverse_comm(int, int *, double *); void print_summary(const std::string pre) const; int get_node_rank(); - std::string get_file_content(const std::string & model); - std::vector get_file_content(const std::vector & models); + std::string get_prog_file_content(const std::string & model); + std::string get_params_file_content(const std::string & model); + std::vector get_prog_file_content(const std::vector & models); + std::vector get_params_file_content(const std::vector & models); protected: virtual void allocate(); - double **scale; + float **scale; private: - deepmd::DeepPot deep_pot; - deepmd::DeepPotModelDevi deep_pot_model_devi; + deepmd::PaddleDeepPot deep_pot; + deepmd::PaddleDeepPotModelDevi deep_pot_model_devi; unsigned numb_models; - double cutoff; + float cutoff; int numb_types; - std::vector > all_force; + std::vector > all_force; std::ofstream fp; int out_freq; std::string out_file; @@ -75,9 +73,9 @@ private: bool multi_models_mod_devi; bool multi_models_no_mod_devi; #ifdef HIGH_PREC - std::vector fparam; - std::vector aparam; - double eps; + std::vector fparam; + std::vector aparam; + float eps; #else std::vector fparam; std::vector aparam; @@ -85,7 +83,7 @@ private: #endif void make_ttm_aparam( #ifdef HIGH_PREC - std::vector & dparam + std::vector & dparam #else std::vector & dparam #endif diff --git a/source/lmp/pppm_dplr.cpp b/source/lmp/pppm_dplr.cpp deleted file mode 100644 index 7b2fbe6e65..0000000000 --- a/source/lmp/pppm_dplr.cpp +++ /dev/null @@ -1,364 +0,0 @@ -#include -#include "pppm_dplr.h" -#include "atom.h" -#include "domain.h" -#include "force.h" -#include "memory.h" -#include "error.h" -#include "math_const.h" -#include "pppm.h" -#include "gridcomm.h" - -using namespace LAMMPS_NS; -using namespace MathConst; - -enum{REVERSE_RHO}; -enum{FORWARD_IK,FORWARD_AD,FORWARD_IK_PERATOM,FORWARD_AD_PERATOM}; - -#define OFFSET 16384 - -#ifdef FFT_SINGLE -#define ZEROF 0.0f -#define ONEF 1.0f -#else -#define ZEROF 0.0 -#define ONEF 1.0 -#endif - - -/* ---------------------------------------------------------------------- */ - -#ifdef OLD_LMP_PPPM -PPPMDPLR::PPPMDPLR(LAMMPS *lmp, int narg, char **arg) : - PPPM(lmp, narg, arg) -#else -PPPMDPLR::PPPMDPLR(LAMMPS *lmp) : - PPPM(lmp) -#endif -{ - triclinic_support = 1; -} - -/* ---------------------------------------------------------------------- */ - -void PPPMDPLR::init() -{ - // DPLR PPPM requires newton on, b/c it computes forces on ghost atoms - - if (force->newton == 0) - error->all(FLERR,"Kspace style pppm/dplr requires newton on"); - - PPPM::init(); - - int nlocal = atom->nlocal; - // cout << " ninit pppm/dplr ---------------------- " << nlocal << endl; - fele.resize(nlocal*3); - fill(fele.begin(), fele.end(), 0.0); -} - -/* ---------------------------------------------------------------------- - compute the PPPM long-range force, energy, virial -------------------------------------------------------------------------- */ - -void PPPMDPLR::compute(int eflag, int vflag) -{ - int i,j; - - // set energy/virial flags - // invoke allocate_peratom() if needed for first time - - ev_init(eflag,vflag); - - if (evflag_atom && !peratom_allocate_flag) allocate_peratom(); - - // if atom count has changed, update qsum and qsqsum - - if (atom->natoms != natoms_original) { - qsum_qsq(); - natoms_original = atom->natoms; - } - - // return if there are no charges - - if (qsqsum == 0.0) return; - - // convert atoms from box to lamda coords - - if (triclinic == 0) boxlo = domain->boxlo; - else { - boxlo = domain->boxlo_lamda; - domain->x2lamda(atom->nlocal); - } - - // extend size of per-atom arrays if necessary - - if (atom->nmax > nmax) { - memory->destroy(part2grid); - nmax = atom->nmax; - memory->create(part2grid,nmax,3,"pppm:part2grid"); - } - - // find grid points for all my particles - // map my particle charge onto my local 3d density grid - - particle_map(); - make_rho(); - - // all procs communicate density values from their ghost cells - // to fully sum contribution in their 3d bricks - // remap from 3d decomposition to FFT decomposition - - gc->reverse_comm_kspace(this,1,sizeof(FFT_SCALAR),REVERSE_RHO, - gc_buf1,gc_buf2,MPI_FFT_SCALAR); - brick2fft(); - - // compute potential gradient on my FFT grid and - // portion of e_long on this proc's FFT grid - // return gradients (electric fields) in 3d brick decomposition - // also performs per-atom calculations via poisson_peratom() - - poisson(); - - // all procs communicate E-field values - // to fill ghost cells surrounding their 3d bricks - - if (differentiation_flag == 1) - gc->forward_comm_kspace(this,1,sizeof(FFT_SCALAR),FORWARD_AD, - gc_buf1,gc_buf2,MPI_FFT_SCALAR); - else - gc->forward_comm_kspace(this,3,sizeof(FFT_SCALAR),FORWARD_IK, - gc_buf1,gc_buf2,MPI_FFT_SCALAR); - - // extra per-atom energy/virial communication - - if (evflag_atom) { - if (differentiation_flag == 1 && vflag_atom) - gc->forward_comm_kspace(this,6,sizeof(FFT_SCALAR),FORWARD_AD_PERATOM, - gc_buf1,gc_buf2,MPI_FFT_SCALAR); - else if (differentiation_flag == 0) - gc->forward_comm_kspace(this,7,sizeof(FFT_SCALAR),FORWARD_IK_PERATOM, - gc_buf1,gc_buf2,MPI_FFT_SCALAR); - } - - // calculate the force on my particles - - fieldforce(); - - // extra per-atom energy/virial communication - - if (evflag_atom) fieldforce_peratom(); - - // sum global energy across procs and add in volume-dependent term - - const double qscale = qqrd2e * scale; - - if (eflag_global) { - double energy_all; - MPI_Allreduce(&energy,&energy_all,1,MPI_DOUBLE,MPI_SUM,world); - energy = energy_all; - - energy *= 0.5*volume; - // do not add self-term, for neutral systems qsum == 0 - // energy -= g_ewald*qsqsum/MY_PIS + - // MY_PI2*qsum*qsum / (g_ewald*g_ewald*volume); - energy *= qscale; - } - - // sum global virial across procs - - if (vflag_global) { - double virial_all[6]; - MPI_Allreduce(virial,virial_all,6,MPI_DOUBLE,MPI_SUM,world); - for (i = 0; i < 6; i++) virial[i] = 0.5*qscale*volume*virial_all[i]; - } - - // per-atom energy/virial - // energy includes self-energy correction - // ntotal accounts for TIP4P tallying eatom/vatom for ghost atoms - - if (evflag_atom) { - double *q = atom->q; - int nlocal = atom->nlocal; - int ntotal = nlocal; - if (tip4pflag) ntotal += atom->nghost; - - if (eflag_atom) { - for (i = 0; i < nlocal; i++) { - eatom[i] *= 0.5; - eatom[i] -= g_ewald*q[i]*q[i]/MY_PIS + MY_PI2*q[i]*qsum / - (g_ewald*g_ewald*volume); - eatom[i] *= qscale; - } - for (i = nlocal; i < ntotal; i++) eatom[i] *= 0.5*qscale; - } - - if (vflag_atom) { - for (i = 0; i < ntotal; i++) - for (j = 0; j < 6; j++) vatom[i][j] *= 0.5*qscale; - } - } - - // 2d slab correction - - if (slabflag == 1) slabcorr(); - - // convert atoms back from lamda to box coords - - if (triclinic) domain->lamda2x(atom->nlocal); -} - -/* ---------------------------------------------------------------------- - interpolate from grid to get electric field & force on my particles for ik -------------------------------------------------------------------------- */ - -void PPPMDPLR::fieldforce_ik() -{ - int i,l,m,n,nx,ny,nz,mx,my,mz; - FFT_SCALAR dx,dy,dz,x0,y0,z0; - FFT_SCALAR ekx,eky,ekz; - - // loop over my charges, interpolate electric field from nearby grid points - // (nx,ny,nz) = global coords of grid pt to "lower left" of charge - // (dx,dy,dz) = distance to "lower left" grid pt - // (mx,my,mz) = global coords of moving stencil pt - // ek = 3 components of E-field on particle - - double *q = atom->q; - double **x = atom->x; - // double **f = atom->f; - - int nlocal = atom->nlocal; - int nghost = atom->nghost; - int nall = nlocal + nghost; - - fele.resize(nlocal*3); - fill(fele.begin(), fele.end(), 0.0); - - for (i = 0; i < nlocal; i++) { - nx = part2grid[i][0]; - ny = part2grid[i][1]; - nz = part2grid[i][2]; - dx = nx+shiftone - (x[i][0]-boxlo[0])*delxinv; - dy = ny+shiftone - (x[i][1]-boxlo[1])*delyinv; - dz = nz+shiftone - (x[i][2]-boxlo[2])*delzinv; - - compute_rho1d(dx,dy,dz); - - ekx = eky = ekz = ZEROF; - for (n = nlower; n <= nupper; n++) { - mz = n+nz; - z0 = rho1d[2][n]; - for (m = nlower; m <= nupper; m++) { - my = m+ny; - y0 = z0*rho1d[1][m]; - for (l = nlower; l <= nupper; l++) { - mx = l+nx; - x0 = y0*rho1d[0][l]; - ekx -= x0*vdx_brick[mz][my][mx]; - eky -= x0*vdy_brick[mz][my][mx]; - ekz -= x0*vdz_brick[mz][my][mx]; - } - } - } - - // convert E-field to force - - const double qfactor = qqrd2e * scale * q[i]; - fele[i*3+0] += qfactor*ekx; - fele[i*3+1] += qfactor*eky; - if (slabflag != 2) fele[i*3+2] += qfactor*ekz; - } -} - -/* ---------------------------------------------------------------------- - interpolate from grid to get electric field & force on my particles for ad -------------------------------------------------------------------------- */ - -void PPPMDPLR::fieldforce_ad() -{ - int i,l,m,n,nx,ny,nz,mx,my,mz; - FFT_SCALAR dx,dy,dz; - FFT_SCALAR ekx,eky,ekz; - double s1,s2,s3; - double sf = 0.0; - double *prd; - - prd = domain->prd; - double xprd = prd[0]; - double yprd = prd[1]; - double zprd = prd[2]; - - double hx_inv = nx_pppm/xprd; - double hy_inv = ny_pppm/yprd; - double hz_inv = nz_pppm/zprd; - - // loop over my charges, interpolate electric field from nearby grid points - // (nx,ny,nz) = global coords of grid pt to "lower left" of charge - // (dx,dy,dz) = distance to "lower left" grid pt - // (mx,my,mz) = global coords of moving stencil pt - // ek = 3 components of E-field on particle - - double *q = atom->q; - double **x = atom->x; - // double **f = atom->f; - - int nlocal = atom->nlocal; - int nghost = atom->nghost; - int nall = nlocal + nghost; - - fele.resize(nlocal*3); - fill(fele.begin(), fele.end(), 0.0); - - for (i = 0; i < nlocal; i++) { - nx = part2grid[i][0]; - ny = part2grid[i][1]; - nz = part2grid[i][2]; - dx = nx+shiftone - (x[i][0]-boxlo[0])*delxinv; - dy = ny+shiftone - (x[i][1]-boxlo[1])*delyinv; - dz = nz+shiftone - (x[i][2]-boxlo[2])*delzinv; - - compute_rho1d(dx,dy,dz); - compute_drho1d(dx,dy,dz); - - ekx = eky = ekz = ZEROF; - for (n = nlower; n <= nupper; n++) { - mz = n+nz; - for (m = nlower; m <= nupper; m++) { - my = m+ny; - for (l = nlower; l <= nupper; l++) { - mx = l+nx; - ekx += drho1d[0][l]*rho1d[1][m]*rho1d[2][n]*u_brick[mz][my][mx]; - eky += rho1d[0][l]*drho1d[1][m]*rho1d[2][n]*u_brick[mz][my][mx]; - ekz += rho1d[0][l]*rho1d[1][m]*drho1d[2][n]*u_brick[mz][my][mx]; - } - } - } - ekx *= hx_inv; - eky *= hy_inv; - ekz *= hz_inv; - - // convert E-field to force and subtract self forces - - const double qfactor = qqrd2e * scale; - - s1 = x[i][0]*hx_inv; - s2 = x[i][1]*hy_inv; - s3 = x[i][2]*hz_inv; - sf = sf_coeff[0]*sin(2*MY_PI*s1); - sf += sf_coeff[1]*sin(4*MY_PI*s1); - sf *= 2*q[i]*q[i]; - fele[i*3+0] += qfactor*(ekx*q[i] - sf); - - sf = sf_coeff[2]*sin(2*MY_PI*s2); - sf += sf_coeff[3]*sin(4*MY_PI*s2); - sf *= 2*q[i]*q[i]; - fele[i*3+1] += qfactor*(eky*q[i] - sf); - - - sf = sf_coeff[4]*sin(2*MY_PI*s3); - sf += sf_coeff[5]*sin(4*MY_PI*s3); - sf *= 2*q[i]*q[i]; - if (slabflag != 2) fele[i*3+2] += qfactor*(ekz*q[i] - sf); - } -} - diff --git a/source/lmp/pppm_dplr.h b/source/lmp/pppm_dplr.h deleted file mode 100644 index 4f22a9c621..0000000000 --- a/source/lmp/pppm_dplr.h +++ /dev/null @@ -1,44 +0,0 @@ -#ifdef KSPACE_CLASS - -KSpaceStyle(pppm/dplr,PPPMDPLR) - -#else - -#ifndef LMP_PPPM_DPLR_H -#define LMP_PPPM_DPLR_H - -#ifdef HIGH_PREC -#define FLOAT_PREC double -#else -#define FLOAT_PREC float -#endif - -#include "pppm.h" -#include -#include - -namespace LAMMPS_NS { - - class PPPMDPLR : public PPPM { -public: -#ifdef OLD_LMP_PPPM - PPPMDPLR(class LAMMPS *, int, char **); -#else - PPPMDPLR(class LAMMPS *); -#endif - virtual ~PPPMDPLR () {}; - void init(); - const std::vector & get_fele() const {return fele;}; -protected: - virtual void compute(int, int); - virtual void fieldforce_ik(); - virtual void fieldforce_ad(); -private: - std::vector fele; - }; - -} - -#endif -#endif - diff --git a/source/op/paddle_ops/srcs/pd_prod_env_mat_multi_devices_cpu.cc b/source/op/paddle_ops/srcs/pd_prod_env_mat_multi_devices_cpu.cc old mode 100644 new mode 100755 index 13592b17bd..334aa0f523 --- a/source/op/paddle_ops/srcs/pd_prod_env_mat_multi_devices_cpu.cc +++ b/source/op/paddle_ops/srcs/pd_prod_env_mat_multi_devices_cpu.cc @@ -1,4 +1,9 @@ +#ifdef ON_INFER +#include "paddle/include/experimental/ext_all.h" +#else #include "paddle/extension.h" +#endif +// #include "paddle/include/experimental/ext_all.h" #include "utilities.h" #include "coord.h" #include "region.h" @@ -190,7 +195,11 @@ std::vector PdProdEnvMatAOpCPUForward( PD_CHECK(sec_r.back() == 0, "Rotational free descriptor only support all-angular information: sel_r should be all zero."); PD_CHECK(natoms_tensor.shape()[0] >= 3, "Number of atoms should be larger than (or equal to) 3"); // Paddle Set device on Python not in custom op - const int *natoms = natoms_tensor.data(); + + // TODO: This code should be removed once cuda issue fixed. + const int* natoms = nullptr; + natoms = natoms_tensor.data(); + int nloc = natoms[0]; int nall = natoms[1]; int ntypes = natoms_tensor.shape()[0] - 2; //nloc and nall mean something. @@ -258,7 +267,6 @@ std::vector PdProdEnvMatAOpCPUForward( std_tensor.data(), type_tensor.data()); })); - return {descrpt_tensor, descrpt_deriv_tensor, rij_tensor, nlist_tensor}; } std::vector PdProdEnvMatAOpForward( diff --git a/source/op/paddle_ops/srcs/pd_prod_env_mat_multi_devices_cuda.cc b/source/op/paddle_ops/srcs/pd_prod_env_mat_multi_devices_cuda.cc index ee714a2d1b..1f8f1e358a 100644 --- a/source/op/paddle_ops/srcs/pd_prod_env_mat_multi_devices_cuda.cc +++ b/source/op/paddle_ops/srcs/pd_prod_env_mat_multi_devices_cuda.cc @@ -1,5 +1,9 @@ #define GOOGLE_CUDA 1 +#ifdef ON_INFER +#include "paddle/include/experimental/ext_all.h" +#else #include "paddle/extension.h" +#endif #include "utilities.h" #include "coord.h" #include "region.h" diff --git a/source/op/paddle_ops/srcs/pd_prod_force_se_a_multi_devices_cpu.cc b/source/op/paddle_ops/srcs/pd_prod_force_se_a_multi_devices_cpu.cc old mode 100644 new mode 100755 index a0ca9218e4..efccc010c2 --- a/source/op/paddle_ops/srcs/pd_prod_force_se_a_multi_devices_cpu.cc +++ b/source/op/paddle_ops/srcs/pd_prod_force_se_a_multi_devices_cpu.cc @@ -1,8 +1,11 @@ #include #include "prod_force.h" #include "prod_force_grad.h" +#ifdef ON_INFER +#include "paddle/include/experimental/ext_all.h" +#else #include "paddle/extension.h" - +#endif #define CHECK_INPUT(x) PD_CHECK(x.place() == paddle::PlaceType::kCPU, #x " must be a CPU Tensor.") #define CHECK_INPUT_READY(x) PD_CHECK(x.is_initialized(), #x " must be initialized before usage.") @@ -55,7 +58,9 @@ int n_r_sel CHECK_INPUT_DIM(natoms_tensor, 1); PD_CHECK(natoms_tensor.shape()[0] >= 3, "number of atoms should be larger than (or equal to) 3"); - const int* natoms = natoms_tensor.data(); + // TODO: This code should be removed once cuda issue fixed. + const int* natoms = nullptr; + natoms = natoms_tensor.data(); int nloc = natoms[0]; int nall = natoms[1]; int nframes = net_deriv_tensor.shape()[0]; @@ -80,12 +85,12 @@ int n_r_sel assert (nnei * 4 == ndescrpt); PD_DISPATCH_FLOATING_TYPES( - net_deriv_tensor.type(), "pd_prod_force_se_a_cpu_forward_kernel", ([&] { + net_deriv_tensor.type(), "pd_prod_force_se_a_cpu_forward_kernel", ([&] { PdProdForceSeAOpForwardCPUKernel( nloc, nall, nframes, ndescrpt, nnei, force_tensor.mutable_data(), net_deriv_tensor.data(), in_deriv_tensor.data(), nlist_tensor.data()); - })); + })); return {force_tensor}; } @@ -145,11 +150,7 @@ int n_r_sel PD_CHECK(natoms_shape[0] >= 3, "number of atoms should be larger than (or equal to) 3"); const int* natoms = nullptr; - if(natoms_tensor.place() != paddle::PlaceType::kCPU){ - natoms = natoms_tensor.copy_to(paddle::PlaceType::kCPU).data(); - }else{ - natoms = natoms_tensor.data(); - } + natoms = natoms_tensor.data(); int nloc = natoms[0]; int nframes = net_deriv_shape[0]; int ndescrpt = net_deriv_shape[1] / nloc; @@ -164,30 +165,16 @@ int n_r_sel std::vector grad_net_shape {nframes, nloc * ndescrpt}; paddle::Tensor grad_net_tensor = paddle::Tensor(paddle::PlaceType::kCPU, grad_net_shape); - if(grad_tensor.place() == paddle::PlaceType::kCPU){ - PD_DISPATCH_FLOATING_TYPES( - grad_tensor.type(), "pd_prod_force_se_a_cpu_backward_kernel", ([&] { - PdProdForceSeAOpCPUBackwardKernel( - nloc, nframes, ndescrpt, nnei, - grad_tensor.data(), - net_deriv_tensor.data(), - in_deriv_tensor.data(), - nlist_tensor.data(), - grad_net_tensor.mutable_data()); - })); - }else{ - PD_DISPATCH_FLOATING_TYPES( - grad_tensor.type(), "pd_prod_force_se_a_cpu_backward_kernel", ([&] { - PdProdForceSeAOpCPUBackwardKernel( - nloc, nframes, ndescrpt, nnei, - grad_tensor.copy_to(paddle::PlaceType::kCPU).data(), - net_deriv_tensor.copy_to(paddle::PlaceType::kCPU).data(), - in_deriv_tensor.copy_to(paddle::PlaceType::kCPU).data(), - nlist_tensor.copy_to(paddle::PlaceType::kCPU).data(), - grad_net_tensor.mutable_data()); - })); - } - + PD_DISPATCH_FLOATING_TYPES( + grad_tensor.type(), "pd_prod_force_se_a_cpu_backward_kernel", ([&] { + PdProdForceSeAOpCPUBackwardKernel( + nloc, nframes, ndescrpt, nnei, + grad_tensor.data(), + net_deriv_tensor.data(), + in_deriv_tensor.data(), + nlist_tensor.data(), + grad_net_tensor.mutable_data()); + })); return {grad_net_tensor}; } diff --git a/source/op/paddle_ops/srcs/pd_prod_force_se_a_multi_devices_cuda.cc b/source/op/paddle_ops/srcs/pd_prod_force_se_a_multi_devices_cuda.cc index 752646d5f2..3d36d8cc8e 100644 --- a/source/op/paddle_ops/srcs/pd_prod_force_se_a_multi_devices_cuda.cc +++ b/source/op/paddle_ops/srcs/pd_prod_force_se_a_multi_devices_cuda.cc @@ -1,6 +1,10 @@ #define GOOGLE_CUDA 1 #include "prod_force.h" +#ifdef ON_INFER +#include "paddle/include/experimental/ext_all.h" +#else #include "paddle/extension.h" +#endif #include #define CHECK_INPUT(x) PD_CHECK(x.place() == paddle::PlaceType::kGPU, #x " must be a GPU Tensor.") diff --git a/source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cpu.cc b/source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cpu.cc old mode 100644 new mode 100755 index 43b8740d0e..3f76426dfc --- a/source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cpu.cc +++ b/source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cpu.cc @@ -1,7 +1,11 @@ #include #include "prod_virial.h" #include "prod_virial_grad.h" +#ifdef ON_INFER +#include "paddle/include/experimental/ext_all.h" +#else #include "paddle/extension.h" +#endif #define CHECK_INPUT(x) PD_CHECK(x.place() == paddle::PlaceType::kCPU, #x " must be a CPU Tensor.") @@ -63,11 +67,7 @@ int n_r_sel PD_CHECK(natoms_tensor.shape()[0] >= 3, "number of atoms should be larger than (or equal to) 3"); // TODO:(jiabin) This code should be removed when virial cuda kernel fixed. const int* natoms = nullptr; - if(natoms_tensor.place() != paddle::PlaceType::kCPU){ - natoms = natoms_tensor.copy_to(paddle::PlaceType::kCPU).data(); - }else{ - natoms = natoms_tensor.data(); - } + natoms = natoms_tensor.data(); int nloc = natoms[0]; int nall = natoms[1]; int nnei = nlist_tensor.shape()[1] / nloc; @@ -86,29 +86,14 @@ int n_r_sel paddle::Tensor virial_tensor = paddle::Tensor(paddle::PlaceType::kCPU, virial_shape); paddle::Tensor atom_virial_tensor = paddle::Tensor(paddle::PlaceType::kCPU, atom_virial_shape); - if(natoms_tensor.place() == paddle::PlaceType::kCPU){ - PD_DISPATCH_FLOATING_TYPES( - net_deriv_tensor.type(), "pd_prod_virial_se_a_cpu_forward_kernel", ([&] { - PdProdVirialSeAOpForwardCPUKernel( - nloc, nall, ndescrpt, nnei, nframes, - virial_tensor.mutable_data(), atom_virial_tensor.mutable_data(), - net_deriv_tensor.data(), in_deriv_tensor.data(), - rij_tensor.data(), nlist_tensor.data()); - })); - }else{ - PD_DISPATCH_FLOATING_TYPES( - net_deriv_tensor.type(), "pd_prod_virial_se_a_cpu_forward_kernel", ([&] { - PdProdVirialSeAOpForwardCPUKernel( - nloc, nall, ndescrpt, nnei, nframes, - virial_tensor.mutable_data(), - atom_virial_tensor.mutable_data(), - net_deriv_tensor.copy_to(paddle::PlaceType::kCPU).data(), - in_deriv_tensor.copy_to(paddle::PlaceType::kCPU).data(), - rij_tensor.copy_to(paddle::PlaceType::kCPU).data(), - nlist_tensor.copy_to(paddle::PlaceType::kCPU).data()); - })); - } - + PD_DISPATCH_FLOATING_TYPES( + net_deriv_tensor.type(), "pd_prod_virial_se_a_cpu_forward_kernel", ([&] { + PdProdVirialSeAOpForwardCPUKernel( + nloc, nall, ndescrpt, nnei, nframes, + virial_tensor.mutable_data(), atom_virial_tensor.mutable_data(), + net_deriv_tensor.data(), in_deriv_tensor.data(), + rij_tensor.data(), nlist_tensor.data()); + })); return {virial_tensor, atom_virial_tensor}; } @@ -174,11 +159,7 @@ int n_r_sel PD_CHECK(natoms_shape[0] >= 3, "number of atoms should be larger than (or equal to) 3"); const int* natoms = nullptr; - if(natoms_tensor.place() != paddle::PlaceType::kCPU){ - natoms = natoms_tensor.copy_to(paddle::PlaceType::kCPU).data(); - }else{ - natoms = natoms_tensor.data(); - } + natoms = natoms_tensor.data(); int nframes = net_deriv_shape[0]; int nloc = natoms[0]; int ndescrpt = net_deriv_shape[1] / nloc; @@ -196,30 +177,16 @@ int n_r_sel std::vector grad_net_shape {nframes, nloc * ndescrpt}; paddle::Tensor grad_net_tensor = paddle::Tensor(paddle::PlaceType::kCPU, grad_net_shape); - if(grad_tensor.place() == paddle::PlaceType::kCPU){ - PD_DISPATCH_FLOATING_TYPES( - grad_tensor.type(), "pd_prod_force_se_a_cpu_backward_kernel", ([&] { - PdProdForceSeAOpCPUBackwardKernel( - nloc, nframes, ndescrpt, nnei, - grad_tensor.data(), - net_deriv_tensor.data(), - in_deriv_tensor.data(), - rij_tensor.data(), nlist_tensor.data(), - grad_net_tensor.mutable_data()); - })); - }else{ - PD_DISPATCH_FLOATING_TYPES( - grad_tensor.type(), "pd_prod_force_se_a_cpu_backward_kernel", ([&] { - PdProdForceSeAOpCPUBackwardKernel( - nloc, nframes, ndescrpt, nnei, - grad_tensor.copy_to(paddle::PlaceType::kCPU).data(), - net_deriv_tensor.copy_to(paddle::PlaceType::kCPU).data(), - in_deriv_tensor.copy_to(paddle::PlaceType::kCPU).data(), - rij_tensor.copy_to(paddle::PlaceType::kCPU).data(), - nlist_tensor.copy_to(paddle::PlaceType::kCPU).data(), - grad_net_tensor.mutable_data()); - })); - } + PD_DISPATCH_FLOATING_TYPES( + grad_tensor.type(), "pd_prod_force_se_a_cpu_backward_kernel", ([&] { + PdProdForceSeAOpCPUBackwardKernel( + nloc, nframes, ndescrpt, nnei, + grad_tensor.data(), + net_deriv_tensor.data(), + in_deriv_tensor.data(), + rij_tensor.data(), nlist_tensor.data(), + grad_net_tensor.mutable_data()); + })); return {grad_net_tensor}; } diff --git a/source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cuda.cc b/source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cuda.cc index 8163e636f3..da32032b30 100644 --- a/source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cuda.cc +++ b/source/op/paddle_ops/srcs/pd_prod_virial_se_a_multi_devices_cuda.cc @@ -1,7 +1,11 @@ #define GOOGLE_CUDA 1 #include #include "prod_virial.h" +#ifdef ON_INFER +#include "paddle/include/experimental/ext_all.h" +#else #include "paddle/extension.h" +#endif #define CHECK_INPUT(x) PD_CHECK(x.place() == paddle::PlaceType::kGPU, #x " must be a GPU Tensor.") #define CHECK_INPUT_DIM(x, value) PD_CHECK(x.shape().size() == value, #x "'s dim should be " #value ".")