diff --git a/.devcontainer/build_cxx.sh b/.devcontainer/build_cxx.sh index 442539301e..007c99b9e5 100755 --- a/.devcontainer/build_cxx.sh +++ b/.devcontainer/build_cxx.sh @@ -11,6 +11,7 @@ mkdir -p ${SCRIPT_PATH}/../buildcxx/ cd ${SCRIPT_PATH}/../buildcxx/ cmake -D ENABLE_TENSORFLOW=ON \ -D ENABLE_PYTORCH=ON \ + -D ENABLE_PADDLE=ON \ -D CMAKE_INSTALL_PREFIX=${SCRIPT_PATH}/../dp/ \ -D LAMMPS_VERSION=stable_29Aug2024_update1 \ -D CMAKE_BUILD_TYPE=Debug \ diff --git a/.github/workflows/suppr.txt b/.github/workflows/suppr.txt new file mode 100644 index 0000000000..17e8c743a0 --- /dev/null +++ b/.github/workflows/suppr.txt @@ -0,0 +1 @@ +leak:libpaddle_inference diff --git a/.github/workflows/test_cc.yml b/.github/workflows/test_cc.yml index f7f3a4f431..f818cc5d3c 100644 --- a/.github/workflows/test_cc.yml +++ b/.github/workflows/test_cc.yml @@ -52,8 +52,13 @@ jobs: LMP_CXX11_ABI_0: 1 CMAKE_GENERATOR: Ninja CXXFLAGS: ${{ matrix.check_memleak && '-fsanitize=leak' || '' }} + LSAN_OPTIONS: suppressions=${{ github.workspace }}/.github/workflows/suppr.txt # test lammps - - run: pytest --cov=deepmd source/lmp/tests + - run: | + cp ${{ github.workspace }}/source/build_tests/paddle_inference_install_dir/paddle/lib/*.so ${{ github.workspace }}/dp_test/lib/ + cp ${{ github.workspace }}/source/build_tests/paddle_inference_install_dir/third_party/install/onednn/lib/* ${{ github.workspace }}/dp_test/lib/ + cp ${{ github.workspace }}/source/build_tests/paddle_inference_install_dir/third_party/install/mklml/lib/* ${{ github.workspace }}/dp_test/lib/ + pytest --cov=deepmd source/lmp/tests env: OMP_NUM_THREADS: 1 TF_INTRA_OP_PARALLELISM_THREADS: 1 @@ -62,12 +67,16 @@ jobs: LD_LIBRARY_PATH: ${{ github.workspace }}/dp_test/lib:${{ github.workspace }}/libtorch/lib if: ${{ !matrix.check_memleak }} # test ipi - - run: pytest --cov=deepmd source/ipi/tests + - run: | + export PATH=${{ github.workspace }}/dp_test/bin:$PATH + cp ${{ github.workspace }}/source/build_tests/paddle_inference_install_dir/paddle/lib/*.so ${{ github.workspace }}/dp_test/lib/ + cp ${{ github.workspace }}/source/build_tests/paddle_inference_install_dir/third_party/install/onednn/lib/* ${{ github.workspace }}/dp_test/lib/ + cp ${{ github.workspace }}/source/build_tests/paddle_inference_install_dir/third_party/install/mklml/lib/* ${{ github.workspace }}/dp_test/lib/ + pytest --cov=deepmd source/ipi/tests env: OMP_NUM_THREADS: 1 TF_INTRA_OP_PARALLELISM_THREADS: 1 TF_INTER_OP_PARALLELISM_THREADS: 1 - PATH: ${{ github.workspace }}/dp_test/bin:$PATH LD_LIBRARY_PATH: ${{ github.workspace }}/dp_test/lib:${{ github.workspace }}/libtorch/lib if: ${{ !matrix.check_memleak }} - uses: codecov/codecov-action@v5 diff --git a/.github/workflows/test_cuda.yml b/.github/workflows/test_cuda.yml index a5b7583656..324987f354 100644 --- a/.github/workflows/test_cuda.yml +++ b/.github/workflows/test_cuda.yml @@ -85,6 +85,9 @@ jobs: - run: | export LD_LIBRARY_PATH=$CUDA_PATH/lib64:/usr/lib/x86_64-linux-gnu/:$GITHUB_WORKSPACE/dp_test/lib:$GITHUB_WORKSPACE/libtorch/lib:$LD_LIBRARY_PATH export PATH=$GITHUB_WORKSPACE/dp_test/bin:$PATH + cp $GITHUB_WORKSPACE/source/build_tests/paddle_inference_install_dir/paddle/lib/* $GITHUB_WORKSPACE/dp_test/lib/ + cp $GITHUB_WORKSPACE/source/build_tests/paddle_inference_install_dir/third_party/install/onednn/lib/* $GITHUB_WORKSPACE/dp_test/lib/ + cp $GITHUB_WORKSPACE/source/build_tests/paddle_inference_install_dir/third_party/install/mklml/lib/* $GITHUB_WORKSPACE/dp_test/lib/ python -m pytest -s source/lmp/tests || (cat log.lammps && exit 1) python -m pytest source/ipi/tests env: diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 63fc189ae0..2b3d1469e6 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,7 +7,7 @@ repos: - id: trailing-whitespace exclude: "^.+\\.pbtxt$" - id: end-of-file-fixer - exclude: "^.+\\.pbtxt$" + exclude: "^.+\\.pbtxt$|deeppot_sea.*\\.json$" - id: check-yaml - id: check-json - id: check-added-large-files @@ -63,7 +63,7 @@ repos: rev: v19.1.6 hooks: - id: clang-format - exclude: ^(source/3rdparty|source/lib/src/gpu/cudart/.+\.inc|.+\.ipynb$) + exclude: ^(source/3rdparty|source/lib/src/gpu/cudart/.+\.inc|.+\.ipynb$|.+\.json$) # markdown, yaml, CSS, javascript - repo: https://github.com/pre-commit/mirrors-prettier rev: v4.0.0-alpha.8 diff --git a/deepmd/pd/utils/serialization.py b/deepmd/pd/utils/serialization.py index 0274608424..f4206ce993 100644 --- a/deepmd/pd/utils/serialization.py +++ b/deepmd/pd/utils/serialization.py @@ -1,5 +1,4 @@ # SPDX-License-Identifier: LGPL-3.0-or-later -import json import paddle @@ -34,14 +33,19 @@ def deserialize_to_file(model_file: str, data: dict) -> None: data : dict The dictionary to be deserialized. """ + paddle.framework.core._set_prim_all_enabled(True) if not model_file.endswith(".json"): raise ValueError("Paddle backend only supports converting .json file") - model = BaseModel.deserialize(data["model"]) + model: paddle.nn.Layer = BaseModel.deserialize(data["model"]) + model.eval() # JIT will happy in this way... - model.model_def_script = json.dumps(data["model_def_script"]) if "min_nbor_dist" in data.get("@variables", {}): - model.min_nbor_dist = float(data["@variables"]["min_nbor_dist"]) - # model = paddle.jit.to_static(model) + model.register_buffer( + "buffer_min_nbor_dist", + paddle.to_tensor( + float(data["@variables"]["min_nbor_dist"]), + ), + ) paddle.set_flags( { "FLAGS_save_cf_stack_op": 1, @@ -49,6 +53,51 @@ def deserialize_to_file(model_file: str, data: dict) -> None: "FLAGS_enable_pir_api": 1, } ) + from paddle.static import ( + InputSpec, + ) + + """ example output shape and dtype of forward + atom_energy: fetch_name_0 (1, 6, 1) float64 + atom_virial: fetch_name_1 (1, 6, 1, 9) float64 + energy: fetch_name_2 (1, 1) float64 + force: fetch_name_3 (1, 6, 3) float64 + mask: fetch_name_4 (1, 6) int32 + virial: fetch_name_5 (1, 9) float64 + """ + model.forward = paddle.jit.to_static( + model.forward, + full_graph=True, + input_spec=[ + InputSpec([1, -1, 3], dtype="float64", name="coord"), + InputSpec([1, -1], dtype="int64", name="atype"), + InputSpec([1, 9], dtype="float64", name="box"), + None, + None, + True, + ], + ) + """ example output shape and dtype of forward_lower + fetch_name_0: atom_energy [1, 192, 1] paddle.float64 + fetch_name_1: energy [1, 1] paddle.float64 + fetch_name_2: extended_force [1, 5184, 3] paddle.float64 + fetch_name_3: extended_virial [1, 5184, 1, 9] paddle.float64 + fetch_name_4: virial [1, 9] paddle.float64 + """ + model.forward_lower = paddle.jit.to_static( + model.forward_lower, + full_graph=True, + input_spec=[ + InputSpec([1, -1, 3], dtype="float64", name="coord"), + InputSpec([1, -1], dtype="int32", name="atype"), + InputSpec([1, -1, -1], dtype="int32", name="nlist"), + None, + None, + None, + True, + None, + ], + ) paddle.jit.save( model, model_file.split(".json")[0], diff --git a/source/CMakeLists.txt b/source/CMakeLists.txt index ca79860450..bec328521d 100644 --- a/source/CMakeLists.txt +++ b/source/CMakeLists.txt @@ -9,6 +9,7 @@ if(ENABLE_TENSORFLOW) # JAX requires TF C interface, contained in TF C++ library set(ENABLE_JAX ON) endif() +option(ENABLE_PADDLE "Enable Paddle interface" OFF) option(BUILD_TESTING "Build test and enable coverage" OFF) set(DEEPMD_C_ROOT "" @@ -26,6 +27,119 @@ if(NOT DEEPMD_C_ROOT) set_if_higher(CMAKE_CXX_STANDARD 14) endif() +if(ENABLE_PADDLE) + if(NOT DEFINED PADDLE_INFERENCE_DIR) + # message(FATAL_ERROR "Make sure PADDLE_INFERENCE_DIR is set when + # ENABLE_PADDLE=ON") + if(USE_CUDA_TOOLKIT) + find_package(CUDAToolkit REQUIRED) + string(REGEX MATCH "^[0-9]+" CUDA_MAJOR_VERSION "${CUDAToolkit_VERSION}") + message(STATUS "Find CUDAToolkit_VERSION: ${CUDAToolkit_VERSION}") + if(CUDA_MAJOR_VERSION VERSION_EQUAL "11") + message( + STATUS + "PADDLE_INFERENCE_DIR is not defined, downloading CUDA11.8 infernece lib to: ${CMAKE_BINARY_DIR}/" + ) + set(DOWNLOAD_URL + "https://paddle-qa.bj.bcebos.com/paddle-pipeline/GITHUB_Docker_Compile_Test_Cuda118_cudnn860_Trt8531_D1/latest/paddle_inference.tgz" + ) + elseif(CUDA_MAJOR_VERSION VERSION_EQUAL "12") + message( + STATUS + "PADDLE_INFERENCE_DIR is not defined, downloading CUDA12.3 infernece lib to: ${CMAKE_BINARY_DIR}/" + ) + set(DOWNLOAD_URL + "https://paddle-qa.bj.bcebos.com/paddle-pipeline/GITHUB_Docker_Compile_Test_Cuda123_cudnn900_Trt8616_D1/latest/paddle_inference.tgz" + ) + else() + message( + FATAL_ERROR + "Paddle inference lib only support cuda 11 or 12, but your CUDA_MAJOR_VERSION is: ${CUDA_MAJOR_VERSION}" + ) + endif() + else() + message( + STATUS + "PADDLE_INFERENCE_DIR is not defined, downloading CPU infernece lib to: ${CMAKE_BINARY_DIR}/" + ) + set(DOWNLOAD_URL + "https://paddle-qa.bj.bcebos.com/paddle-pipeline/GITHUB_Docker_Compile_Test_Cpu_Mkl_Avx_D1/latest/paddle_inference.tgz" + ) + endif() + set(TGZ_FILE "${CMAKE_BINARY_DIR}/paddle_inference.tgz") + set(EXTRACTED_DIR "${CMAKE_BINARY_DIR}/paddle_inference_install_dir") + file(DOWNLOAD ${DOWNLOAD_URL} ${TGZ_FILE}) + message(STATUS "Downloading finished, extracting...") + execute_process(COMMAND ${CMAKE_COMMAND} -E tar -xzvf ${TGZ_FILE} + OUTPUT_QUIET) + file(REMOVE ${TGZ_FILE}) + set(PADDLE_INFERENCE_DIR + ${EXTRACTED_DIR} + CACHE PATH + "Path to 'paddle_inference_install_dir' or 'paddle_inference'") + else() + message( + STATUS "PADDLE_INFERENCE_DIR is already defined: ${PADDLE_INFERENCE_DIR}") + endif() + + link_directories( + ${PADDLE_INFERENCE_DIR}/paddle/lib + ${PADDLE_INFERENCE_DIR}/third_party/install/onednn/lib + ${PADDLE_INFERENCE_DIR}/third_party/install/mklml/lib) + + # Check and append to existing CMAKE_BUILD_RPATH + if(DEFINED CMAKE_BUILD_RPATH) + list(APPEND CMAKE_BUILD_RPATH "${PADDLE_INFERENCE_DIR}/paddle/lib" + "${PADDLE_INFERENCE_DIR}/third_party/install/onednn/lib" + "${PADDLE_INFERENCE_DIR}/third_party/install/mklml/lib") + else() + set(CMAKE_BUILD_RPATH + "${PADDLE_INFERENCE_DIR}/paddle/lib" + "${PADDLE_INFERENCE_DIR}/third_party/install/onednn/lib" + "${PADDLE_INFERENCE_DIR}/third_party/install/mklml/lib") + endif() + + # Check and append to existing CMAKE_INSTALL_RPATH + if(DEFINED CMAKE_INSTALL_RPATH) + list(APPEND CMAKE_INSTALL_RPATH "${PADDLE_INFERENCE_DIR}/paddle/lib" + "${PADDLE_INFERENCE_DIR}/third_party/install/onednn/lib" + "${PADDLE_INFERENCE_DIR}/third_party/install/mklml/lib") + else() + set(CMAKE_INSTALL_RPATH + "${PADDLE_INFERENCE_DIR}/paddle/lib" + "${PADDLE_INFERENCE_DIR}/third_party/install/onednn/lib" + "${PADDLE_INFERENCE_DIR}/third_party/install/mklml/lib") + endif() + + set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE) + message(STATUS "Final PADDLE_INFERENCE_DIR is set to ${PADDLE_INFERENCE_DIR}") + + set(PADDLE_INFERENCE_DIR + ${PADDLE_INFERENCE_DIR} + CACHE PATH "Path to 'paddle_inference_install_dir' or 'paddle_inference'") + + # used in api_cc + set(PADDLE_LIBRARIES + "${PADDLE_INFERENCE_DIR}/paddle/lib/libpaddle_inference.so" + CACHE PATH "Path to libpaddle_inference.so") + + include_directories("${PADDLE_INFERENCE_DIR}/") + set(PADDLE_LIB_THIRD_PARTY_PATH + "${PADDLE_INFERENCE_DIR}/third_party/install/") + + include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/include") + include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/include") + include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/include") + include_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/include") + + link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}protobuf/lib") + link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}glog/lib") + link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}gflags/lib") + link_directories("${PADDLE_LIB_THIRD_PARTY_PATH}xxhash/lib") + link_directories("${PADDLE_INFERENCE_DIR}/paddle/lib") + # if (USE_ROCM_TOOLKIT) add_definitions(-D_GLIBCXX_USE_CXX11_ABI=1) endif() +endif(ENABLE_PADDLE) + if(BUILD_TESTING) enable_testing() add_subdirectory(${CMAKE_SOURCE_DIR}/cmake/coverage_config coverage_config) @@ -279,9 +393,13 @@ if(NOT DEEPMD_C_ROOT) if(ENABLE_JAX) message(STATUS "- JAX") endif() + if(ENABLE_PADDLE) + message(STATUS "- Paddle") + endif() if(NOT ENABLE_TENSORFLOW AND NOT ENABLE_PYTORCH AND NOT ENABLE_JAX + AND NOT ENABLE_PADDLE AND NOT BUILD_PY_IF) message(FATAL_ERROR "No backend is enabled.") endif() diff --git a/source/api_cc/CMakeLists.txt b/source/api_cc/CMakeLists.txt index 32d2d7e18a..431a5481a0 100644 --- a/source/api_cc/CMakeLists.txt +++ b/source/api_cc/CMakeLists.txt @@ -27,6 +27,14 @@ if(ENABLE_JAX) target_link_libraries(${libname} PRIVATE TensorFlow::tensorflow_c) target_compile_definitions(${libname} PRIVATE BUILD_JAX) endif() +if(ENABLE_PADDLE AND NOT BUILD_PY_IF) + target_link_libraries(${libname} PUBLIC "${PADDLE_LIBRARIES}") + target_compile_definitions(${libname} PUBLIC BUILD_PADDLE) + if(DP_VARIANT STREQUAL "rocm") + target_link_libraries(${libname} + PUBLIC "${hip_LIB_INSTALL_DIR}/libgalaxyhip.so") + endif() +endif() target_include_directories( ${libname} diff --git a/source/api_cc/include/DeepPotPD.h b/source/api_cc/include/DeepPotPD.h new file mode 100644 index 0000000000..ec43300ca0 --- /dev/null +++ b/source/api_cc/include/DeepPotPD.h @@ -0,0 +1,398 @@ +// SPDX-License-Identifier: LGPL-3.0-or-later +#pragma once + +#include + +#include "DeepPot.h" + +namespace deepmd { +/** + * @brief Paddle implementation for Deep Potential. + **/ +class DeepPotPD : public DeepPotBackend { + public: + /** + * @brief DP constructor without initialization. + **/ + DeepPotPD(); + virtual ~DeepPotPD(); + /** + * @brief DP constructor with initialization. + * @param[in] model The name of the frozen model file. + * @param[in] gpu_rank The GPU rank. Default is 0. + * @param[in] file_content The content of the model file. If it is not empty, + *DP will read from the string instead of the file. + **/ + DeepPotPD(const std::string& model, + const int& gpu_rank = 0, + const std::string& file_content = ""); + /** + * @brief Initialize the DP. + * @param[in] model The name of the frozen model file. + * @param[in] gpu_rank The GPU rank. Default is 0. + * @param[in] file_content The content of the model file. If it is not empty, + *DP will read from the string instead of the file. + **/ + void init(const std::string& model, + const int& gpu_rank = 0, + const std::string& file_content = ""); + + private: + /** + * @brief Evaluate the energy, force, virial, atomic energy, and atomic virial + *by using this DP. + * @param[out] ener The system energy. + * @param[out] force The force on each atom. + * @param[out] virial The virial. + * @param[out] atom_energy The atomic energy. + * @param[out] atom_virial The atomic virial. + * @param[in] coord The coordinates of atoms. The array should be of size + *nframes x natoms x 3. + * @param[in] atype The atom types. The list should contain natoms ints. + * @param[in] box The cell of the region. The array should be of size nframes + *x 9. + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. + * @param[in] atomic Whether to compute the atomic energy and virial. + **/ + template + void compute(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + /** + * @brief Evaluate the energy, force, virial, atomic energy, and atomic virial + *by using this DP. + * @param[out] ener The system energy. + * @param[out] force The force on each atom. + * @param[out] virial The virial. + * @param[out] atom_energy The atomic energy. + * @param[out] atom_virial The atomic virial. + * @param[in] coord The coordinates of atoms. The array should be of size + *nframes x natoms x 3. + * @param[in] atype The atom types. The list should contain natoms ints. + * @param[in] box The cell of the region. The array should be of size nframes + *x 9. + * @param[in] nghost The number of ghost atoms. + * @param[in] lmp_list The input neighbour list. + * @param[in] ago Update the internal neighbour list if ago is 0. + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. + * @param[in] atomic Whether to compute the atomic energy and virial. + **/ + template + void compute(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& lmp_list, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + /** + * @brief Evaluate the energy, force, and virial with the mixed type + *by using this DP. + * @param[out] ener The system energy. + * @param[out] force The force on each atom. + * @param[out] virial The virial. + * @param[in] nframes The number of frames. + * @param[in] coord The coordinates of atoms. The array should be of size + *nframes x natoms x 3. + * @param[in] atype The atom types. The array should be of size nframes x + *natoms. + * @param[in] box The cell of the region. The array should be of size nframes + *x 9. + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. + * @param[in] atomic Whether to compute the atomic energy and virial. + **/ + template + void compute_mixed_type(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + /** + * @brief Evaluate the energy, force, and virial with the mixed type + *by using this DP. + * @param[out] ener The system energy. + * @param[out] force The force on each atom. + * @param[out] virial The virial. + * @param[out] atom_energy The atomic energy. + * @param[out] atom_virial The atomic virial. + * @param[in] nframes The number of frames. + * @param[in] coord The coordinates of atoms. The array should be of size + *nframes x natoms x 3. + * @param[in] atype The atom types. The array should be of size nframes x + *natoms. + * @param[in] box The cell of the region. The array should be of size nframes + *x 9. + * @param[in] fparam The frame parameter. The array can be of size : + * nframes x dim_fparam. + * dim_fparam. Then all frames are assumed to be provided with the same + *fparam. + * @param[in] aparam The atomic parameter The array can be of size : + * nframes x natoms x dim_aparam. + * natoms x dim_aparam. Then all frames are assumed to be provided with the + *same aparam. + * @param[in] atomic Whether to compute the atomic energy and virial. + **/ + template + void compute_mixed_type(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + + public: + /** + * @brief Get the cutoff radius. + * @return The cutoff radius. + **/ + double cutoff() const { + assert(inited); + return rcut; + }; + /** + * @brief Get the number of types. + * @return The number of types. + **/ + int numb_types() const { + assert(inited); + return ntypes; + }; + /** + * @brief Get the number of types with spin. + * @return The number of types with spin. + **/ + int numb_types_spin() const { + assert(inited); + return ntypes_spin; + }; + /** + * @brief Get the dimension of the frame parameter. + * @return The dimension of the frame parameter. + **/ + int dim_fparam() const { + assert(inited); + return dfparam; + }; + /** + * @brief Get the dimension of the atomic parameter. + * @return The dimension of the atomic parameter. + **/ + int dim_aparam() const { + assert(inited); + return daparam; + }; + /** + * @brief Get the type map (element name of the atom types) of this model. + * @param[out] type_map The type map of this model. + **/ + void get_type_map(std::string& type_map); + + /** + * @brief Get the buffer arraay of this model. + * @param[in] buffer_name Buffer name. + * @param[out] buffer_array Buffer array. + **/ + template + void get_buffer(const std::string& buffer_name, + std::vector& buffer_array); + + /** + * @brief Get the buffer scalar of this model. + * @param[in] buffer_name Buffer name. + * @param[out] buffer_scalar Buffer scalar. + **/ + template + void get_buffer(const std::string& buffer_name, BUFFERTYPE& buffer_scalar); + + /** + * @brief Get whether the atom dimension of aparam is nall instead of fparam. + * @param[out] aparam_nall whether the atom dimension of aparam is nall + *instead of fparam. + **/ + bool is_aparam_nall() const { + assert(inited); + return aparam_nall; + }; + + /** + * @brief Print the shape of given tensor. + * @param[in] x Tensor x. + **/ + void print_shape(const paddle_infer::Tensor& x) const { + std::vector x_shape = x.shape(); + std::string shape_str = "["; + for (int i = 0, n = x_shape.size(); i < n; ++i) { + if (i > 0) { + shape_str += ", "; + } + shape_str += std::to_string(x_shape[i]); + } + shape_str += "]"; + std::cout << shape_str; + }; + + /** + * @brief Compute the number of elements in a tensor. + * @param[in] x Tensor x. + **/ + int numel(const paddle_infer::Tensor& x) const { + // TODO: There might be a overflow problem here for multiply int numbers. + int ret = 1; + std::vector x_shape = x.shape(); + for (std::size_t i = 0, n = x_shape.size(); i < n; ++i) { + ret *= x_shape[i]; + } + return ret; + }; + + // forward to template class + void computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + void computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + void computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& inlist, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + void computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& inlist, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + void computew_mixed_type(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + void computew_mixed_type(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + + private: + int num_intra_nthreads, num_inter_nthreads; + bool inited; + int ntypes; + int ntypes_spin; + int dfparam; + int daparam; + int aparam_nall; + // copy neighbor list info from host + // config & predictor for model.forward + std::shared_ptr config; + std::shared_ptr predictor; + // config & predictor for model.forward_lower + std::shared_ptr config_fl; + std::shared_ptr predictor_fl; + + double rcut; + NeighborListData nlist_data; + int max_num_neighbors; + int gpu_id; + // use int instead bool for problems may meets with vector + int do_message_passing; // 1:dpa2 model 0:others + bool gpu_enabled; + std::unique_ptr firstneigh_tensor; + // std::unordered_map comm_dict; # Not used yet +}; + +} // namespace deepmd diff --git a/source/api_cc/include/version.h.in b/source/api_cc/include/version.h.in index 26b0c1be48..4be0589a30 100644 --- a/source/api_cc/include/version.h.in +++ b/source/api_cc/include/version.h.in @@ -10,4 +10,5 @@ const std::string global_git_branch="@GIT_BRANCH@"; const std::string global_tf_include_dir="@TensorFlow_INCLUDE_DIRS@"; const std::string global_tf_lib="@TensorFlow_LIBRARY@"; const std::string global_pt_lib="@TORCH_LIBRARIES@"; +const std::string global_pd_lib="@PADDLE_LIBRARIES@"; const std::string global_model_version="@MODEL_VERSION@"; diff --git a/source/api_cc/src/DeepPot.cc b/source/api_cc/src/DeepPot.cc index 29f8b99cfd..0964d349a0 100644 --- a/source/api_cc/src/DeepPot.cc +++ b/source/api_cc/src/DeepPot.cc @@ -15,6 +15,9 @@ #if defined(BUILD_TENSORFLOW) || defined(BUILD_JAX) #include "DeepPotJAX.h" #endif +#ifdef BUILD_PADDLE +#include "DeepPotPD.h" +#endif #include "device.h" using namespace deepmd; @@ -53,7 +56,11 @@ void DeepPot::init(const std::string& model, throw deepmd::deepmd_exception("PyTorch backend is not built"); #endif } else if (deepmd::DPBackend::Paddle == backend) { +#ifdef BUILD_PADDLE + dp = std::make_shared(model, gpu_rank, file_content); +#else throw deepmd::deepmd_exception("PaddlePaddle backend is not supported yet"); +#endif } else if (deepmd::DPBackend::JAX == backend) { #if defined(BUILD_TENSORFLOW) || defined(BUILD_JAX) dp = std::make_shared(model, gpu_rank, file_content); diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc new file mode 100644 index 0000000000..c5f9391ca9 --- /dev/null +++ b/source/api_cc/src/DeepPotPD.cc @@ -0,0 +1,551 @@ +// SPDX-License-Identifier: LGPL-3.0-or-later +#ifdef BUILD_PADDLE +#include "DeepPotPD.h" + +#include +#include + +#include "common.h" +#include "device.h" +#include "errors.h" + +using namespace deepmd; + +std::vector createNlistTensorPD( + const std::vector>& data) { + std::vector ret; + for (const auto& row : data) { + ret.insert(ret.end(), row.begin(), row.end()); + } + return ret; +} + +DeepPotPD::DeepPotPD() : inited(false) {} +DeepPotPD::DeepPotPD(const std::string& model, + const int& gpu_rank, + const std::string& file_content) + : inited(false) { + try { + init(model, gpu_rank, file_content); + } catch (...) { + // Clean up and rethrow, as the destructor will not be called + throw; + } +} +void DeepPotPD::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; + } + // NOTE: There is no custom operators need to be loaded now. + // deepmd::load_op_library(); + + // NOTE: Only support 1 GPU now. + int gpu_num = 1; + if (gpu_num > 0) { + gpu_id = gpu_rank % gpu_num; + } else { + gpu_id = 0; + } + + // initialize inference config + config = std::make_shared(); + config->DisableGlogInfo(); + config->EnableNewExecutor(true); + config->EnableNewIR(true); + config->EnableCustomPasses({"add_shadow_output_after_dead_parameter_pass"}, + true); + + // initialize inference config_fl + config_fl = std::make_shared(); + config_fl->DisableGlogInfo(); + config_fl->EnableNewExecutor(true); + config_fl->EnableNewIR(true); + config_fl->EnableCustomPasses({"add_shadow_output_after_dead_parameter_pass"}, + true); + + // loading inference model + std::string pdmodel_path, fl_pdmodel_path; + std::string pdiparams_path, fl_pdiparams_path; + if (model.find(".json") != std::string::npos) { + // load inference of model.forward + pdmodel_path = model; + pdiparams_path = model; + pdiparams_path.replace(pdiparams_path.find(".json"), 5, + std::string(".pdiparams")); + + // load inference of model.forward_lower + fl_pdmodel_path = pdmodel_path; + size_t last_slash_pos = fl_pdmodel_path.rfind('/'); + size_t dot_pos = fl_pdmodel_path.rfind('.'); + std::string filename = fl_pdmodel_path.substr(last_slash_pos + 1, + dot_pos - last_slash_pos - 1); + filename = filename + "." + "forward_lower"; + fl_pdmodel_path.replace(last_slash_pos + 1, dot_pos - last_slash_pos - 1, + filename); + + fl_pdiparams_path = pdiparams_path; + last_slash_pos = fl_pdiparams_path.rfind('/'); + dot_pos = fl_pdiparams_path.rfind('.'); + filename = fl_pdiparams_path.substr(last_slash_pos + 1, + dot_pos - last_slash_pos - 1); + filename = filename + "." + "forward_lower"; + fl_pdiparams_path.replace(last_slash_pos + 1, dot_pos - last_slash_pos - 1, + filename); + + } else if (model.find(".pdmodel") != std::string::npos) { + pdmodel_path = model; + pdiparams_path = model; + pdiparams_path.replace(pdiparams_path.find(".pdmodel"), 8, + std::string(".pdiparams")); + } else { + throw deepmd::deepmd_exception("Given inference model: " + model + + " do not exist, please check it."); + } + const char* use_cuda_toolkit = std::getenv("USE_CUDA_TOOLKIT"); + gpu_enabled = (use_cuda_toolkit && (std::string(use_cuda_toolkit) == "1")); + config->SetModel(pdmodel_path, pdiparams_path); + config_fl->SetModel(fl_pdmodel_path, fl_pdiparams_path); + if (!gpu_enabled) { + config->DisableGpu(); + config_fl->DisableGpu(); + std::cout << "load model from: " << model << " to cpu " << std::endl; + } else { + config->EnableUseGpu(4096, 0); + config_fl->EnableUseGpu(4096, 0); + std::cout << "load model from: " << model << " to gpu:" << gpu_id + << std::endl; + } + + // NOTE: Both set to 1 now. + // get_env_nthreads(num_intra_nthreads, + // num_inter_nthreads); // need to be fixed as + // // DP_INTRA_OP_PARALLELISM_THREADS + // num_intra_nthreads = 1; + num_inter_nthreads = 1; + if (num_inter_nthreads) { + config->SetCpuMathLibraryNumThreads(num_inter_nthreads); + config_fl->SetCpuMathLibraryNumThreads(num_inter_nthreads); + } + + predictor = paddle_infer::CreatePredictor(*config); + predictor_fl = paddle_infer::CreatePredictor(*config_fl); + + // initialize hyper params from model buffers + ntypes_spin = 0; + DeepPotPD::get_buffer("buffer_has_message_passing", do_message_passing); + DeepPotPD::get_buffer("buffer_rcut", rcut); + DeepPotPD::get_buffer("buffer_ntypes", ntypes); + DeepPotPD::get_buffer("buffer_dfparam", dfparam); + DeepPotPD::get_buffer("buffer_daparam", daparam); + DeepPotPD::get_buffer("buffer_aparam_nall", aparam_nall); + inited = true; +} +DeepPotPD::~DeepPotPD() {} + +template +void DeepPotPD::compute(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& lmp_list, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + int natoms = atype.size(); + // select real atoms + std::vector dcoord, dforce, aparam_, datom_energy, datom_virial; + std::vector datype, fwd_map, bkw_map; + int nghost_real, nall_real, nloc_real; + int nall = natoms; + select_real_atoms_coord(dcoord, datype, aparam_, nghost_real, fwd_map, + bkw_map, nall_real, nloc_real, coord, atype, aparam, + nghost, ntypes, 1, daparam, nall, aparam_nall); + int nloc = nall_real - nghost_real; + int nframes = 1; + std::vector coord_wrapped = dcoord; + auto coord_wrapped_Tensor = predictor_fl->GetInputHandle("coord"); + coord_wrapped_Tensor->Reshape({1, nall_real, 3}); + coord_wrapped_Tensor->CopyFromCpu(coord_wrapped.data()); + + auto atype_Tensor = predictor_fl->GetInputHandle("atype"); + atype_Tensor->Reshape({1, nall_real}); + atype_Tensor->CopyFromCpu(datype.data()); + + if (ago == 0) { + nlist_data.copy_from_nlist(lmp_list); + nlist_data.shuffle_exclude_empty(fwd_map); + nlist_data.padding(); + if (do_message_passing == 1 && nghost > 0) { + throw deepmd::deepmd_exception( + "(do_message_passing == 1 && nghost > 0) is not supported yet."); + // int nswap = lmp_list.nswap; + // auto sendproc_tensor = predictor_fl->GetInputHandle("sendproc"); + // sendproc_tensor->Reshape({nswap}); + // sendproc_tensor->CopyFromCpu(lmp_list.sendproc); + // auto recvproc_tensor = predictor_fl->GetInputHandle("recvproc"); + // recvproc_tensor->Reshape({nswap}); + // recvproc_tensor->CopyFromCpu(lmp_list.recvproc); + // auto firstrecv_tensor = predictor_fl->GetInputHandle("firstrecv"); + // firstrecv_tensor->Reshape({nswap}); + // firstrecv_tensor->CopyFromCpu(lmp_list.firstrecv); + // auto recvnum_tensor = predictor_fl->GetInputHandle("recvnum"); + // recvnum_tensor->Reshape({nswap}); + // recvnum_tensor->CopyFromCpu(lmp_list.recvnum); + // auto sendnum_tensor = predictor_fl->GetInputHandle("sendnum"); + // sendnum_tensor->Reshape({nswap}); + // sendnum_tensor->CopyFromCpu(lmp_list.sendnum); + // auto communicator_tensor = + // predictor_fl->GetInputHandle("communicator"); + // communicator_tensor->Reshape({1}); + // communicator_tensor->CopyFromCpu(static_cast(lmp_list.world)); + // auto sendlist_tensor = predictor_fl->GetInputHandle("sendlist"); + + // int total_send = + // std::accumulate(lmp_list.sendnum, lmp_list.sendnum + nswap, 0); + } + if (do_message_passing == 1 && nghost == 0) { + throw deepmd::deepmd_exception( + "(do_message_passing == 1 && nghost == 0) is not supported yet."); + } + } + std::vector firstneigh = createNlistTensorPD(nlist_data.jlist); + firstneigh_tensor = predictor_fl->GetInputHandle("nlist"); + firstneigh_tensor->Reshape({1, nloc, (int)firstneigh.size() / (int)nloc}); + firstneigh_tensor->CopyFromCpu(firstneigh.data()); + bool do_atom_virial_tensor = atomic; + std::unique_ptr fparam_tensor; + if (!fparam.empty()) { + fparam_tensor = predictor_fl->GetInputHandle("fparam"); + fparam_tensor->Reshape({1, static_cast(fparam.size())}); + fparam_tensor->CopyFromCpu((fparam.data())); + } + std::unique_ptr aparam_tensor; + if (!aparam_.empty()) { + aparam_tensor = predictor_fl->GetInputHandle("aparam"); + aparam_tensor->Reshape( + {1, lmp_list.inum, static_cast(aparam_.size()) / lmp_list.inum}); + aparam_tensor->CopyFromCpu((aparam_.data())); + } + + if (!predictor_fl->Run()) { + throw deepmd::deepmd_exception("Paddle inference run failed"); + } + auto output_names = predictor_fl->GetOutputNames(); + + auto energy_ = predictor_fl->GetOutputHandle(output_names.at(1)); + auto force_ = predictor_fl->GetOutputHandle(output_names.at(2)); + auto virial_ = predictor_fl->GetOutputHandle(output_names.at(4)); + int output_energy_size = numel(*energy_); + int output_force_size = numel(*force_); + int output_virial_size = numel(*virial_); + // output energy + ener.resize(output_energy_size); + energy_->CopyToCpu(ener.data()); + + // output force + dforce.resize(output_force_size); + force_->CopyToCpu(dforce.data()); + + // output virial + virial.resize(output_virial_size); + virial_->CopyToCpu(virial.data()); + + // bkw map + force.resize(static_cast(nframes) * fwd_map.size() * 3); + select_map(force, dforce, bkw_map, 3, nframes, fwd_map.size(), + nall_real); + if (atomic) { + auto atom_virial_ = predictor_fl->GetOutputHandle(output_names.at(3)); + auto atom_energy_ = predictor_fl->GetOutputHandle(output_names.at(0)); + datom_energy.resize(nall_real, + 0.0); // resize to nall to be consistenet with TF. + atom_energy_->CopyToCpu(datom_energy.data()); + datom_virial.resize(numel(*atom_virial_)); + atom_virial_->CopyToCpu(datom_virial.data()); + atom_energy.resize(static_cast(nframes) * fwd_map.size()); + atom_virial.resize(static_cast(nframes) * fwd_map.size() * 9); + select_map(atom_energy, datom_energy, bkw_map, 1, nframes, + fwd_map.size(), nall_real); + select_map(atom_virial, datom_virial, bkw_map, 9, nframes, + fwd_map.size(), nall_real); + } +} +template void DeepPotPD::compute>( + std::vector& dener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& lmp_list, + const int& ago, + const std::vector& fparam, + const std::vector& aparam_, + const bool atomic); + +template void DeepPotPD::compute>( + std::vector& dener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& lmp_list, + const int& ago, + const std::vector& fparam, + const std::vector& aparam_, + const bool atomic); + +// ENERGYVTYPE: std::vector or ENERGYTYPE +template +void DeepPotPD::compute(ENERGYVTYPE& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + // select real atoms + std::vector coord_wrapped = coord; + int natoms = atype.size(); + int nframes = 1; + auto coord_wrapped_Tensor = predictor->GetInputHandle("coord"); + coord_wrapped_Tensor->Reshape({1, natoms, 3}); + coord_wrapped_Tensor->CopyFromCpu(coord_wrapped.data()); + + std::vector atype_64(atype.begin(), atype.end()); + auto atype_Tensor = predictor->GetInputHandle("atype"); + atype_Tensor->Reshape({1, natoms}); + atype_Tensor->CopyFromCpu(atype_64.data()); + + std::unique_ptr box_Tensor; + if (!box.empty()) { + box_Tensor = predictor->GetInputHandle("box"); + box_Tensor->Reshape({1, 9}); + box_Tensor->CopyFromCpu((box.data())); + } + std::unique_ptr fparam_tensor; + if (!fparam.empty()) { + fparam_tensor = predictor->GetInputHandle("box"); + fparam_tensor->Reshape({1, static_cast(fparam.size())}); + fparam_tensor->CopyFromCpu((fparam.data())); + } + std::unique_ptr aparam_tensor; + if (!aparam.empty()) { + aparam_tensor = predictor->GetInputHandle("box"); + aparam_tensor->Reshape( + {1, natoms, static_cast(aparam.size()) / natoms}); + aparam_tensor->CopyFromCpu((aparam.data())); + } + + bool do_atom_virial_tensor = atomic; + if (!predictor->Run()) { + throw deepmd::deepmd_exception("Paddle inference run failed"); + } + + auto output_names = predictor->GetOutputNames(); + auto energy_ = predictor->GetOutputHandle(output_names.at(2)); + auto force_ = predictor->GetOutputHandle(output_names.at(3)); + auto virial_ = predictor->GetOutputHandle(output_names.at(5)); + + int enery_numel = numel(*energy_); + assert(enery_numel > 0); + ener.resize(enery_numel); + energy_->CopyToCpu(ener.data()); + + int force_numel = numel(*force_); + assert(force_numel > 0); + force.resize(force_numel); + force_->CopyToCpu(force.data()); + + int virial_numel = numel(*virial_); + assert(virial_numel > 0); + virial.resize(virial_numel); + virial_->CopyToCpu(virial.data()); + + if (atomic) { + auto atom_energy_ = predictor->GetOutputHandle(output_names.at(0)); + auto atom_virial_ = predictor->GetOutputHandle(output_names.at(1)); + int atom_energy_numel = numel(*atom_energy_); + int atom_virial_numel = numel(*atom_virial_); + assert(atom_energy_numel > 0); + assert(atom_virial_numel > 0); + atom_energy.resize(atom_energy_numel); + atom_energy_->CopyToCpu(atom_energy.data()); + atom_virial.resize(atom_virial_numel); + atom_virial_->CopyToCpu(atom_virial.data()); + } +} + +template void DeepPotPD::compute>( + std::vector& ener, + std::vector& dforce, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& dcoord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +template void DeepPotPD::compute>( + std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& dcoord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic); + +/* type_map is regarded as a special string buffer +that need to be postprocessed */ +void DeepPotPD::get_type_map(std::string& type_map) { + auto type_map_tensor = predictor->GetOutputHandle("buffer_type_map"); + int type_map_size = numel(*type_map_tensor); + + std::vector type_map_arr(type_map_size, 0); + type_map_tensor->CopyToCpu(type_map_arr.data()); + for (auto char_c : type_map_arr) { + type_map += std::string(1, char_c); + } +} + +/* general function except for string buffer */ +template +void DeepPotPD::get_buffer(const std::string& buffer_name, + std::vector& buffer_array) { + auto buffer_tensor = predictor->GetOutputHandle(buffer_name); + int buffer_size = numel(*buffer_tensor); + buffer_array.resize(buffer_size); + buffer_tensor->CopyToCpu(buffer_array.data()); +} + +template +void DeepPotPD::get_buffer(const std::string& buffer_name, + BUFFERTYPE& buffer_scalar) { + std::vector buffer_array(1); + DeepPotPD::get_buffer(buffer_name, buffer_array); + buffer_scalar = buffer_array[0]; +} + +// forward to template method +void DeepPotPD::computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, + fparam, aparam, atomic); +} +void DeepPotPD::computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, + fparam, aparam, atomic); +} +void DeepPotPD::computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& inlist, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, + nghost, inlist, ago, fparam, aparam, atomic); +} +void DeepPotPD::computew(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const int nghost, + const InputNlist& inlist, + const int& ago, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + compute(ener, force, virial, atom_energy, atom_virial, coord, atype, box, + nghost, inlist, ago, fparam, aparam, atomic); +} +void DeepPotPD::computew_mixed_type(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + throw deepmd::deepmd_exception( + "computew_mixed_type is not implemented in paddle backend yet"); +} +void DeepPotPD::computew_mixed_type(std::vector& ener, + std::vector& force, + std::vector& virial, + std::vector& atom_energy, + std::vector& atom_virial, + const int& nframes, + const std::vector& coord, + const std::vector& atype, + const std::vector& box, + const std::vector& fparam, + const std::vector& aparam, + const bool atomic) { + throw deepmd::deepmd_exception( + "computew_mixed_type is not implemented in paddle backend yet"); +} +#endif diff --git a/source/api_cc/src/common.cc b/source/api_cc/src/common.cc index d3cad083bd..70755c901a 100644 --- a/source/api_cc/src/common.cc +++ b/source/api_cc/src/common.cc @@ -1398,6 +1398,9 @@ void deepmd::print_summary(const std::string& pre) { #endif #ifdef BUILD_PYTORCH std::cout << pre << "build with pt lib: " + global_pt_lib << "\n"; +#endif +#ifdef BUILD_PADDLE + std::cout << pre << "build with pd lib: " + global_pd_lib << "\n"; #endif std::cout << pre << "set tf intra_op_parallelism_threads: " << num_intra_nthreads @@ -1415,6 +1418,9 @@ deepmd::DPBackend deepmd::get_backend(const std::string& model) { } else if (model.length() >= 11 && model.substr(model.length() - 11) == ".savedmodel") { return deepmd::DPBackend::JAX; + } else if ((model.length() >= 5 && + model.substr(model.length() - 5) == ".json")) { + return deepmd::DPBackend::Paddle; } throw deepmd::deepmd_exception("Unsupported model file format"); } diff --git a/source/api_cc/tests/test_deeppot_pd.cc b/source/api_cc/tests/test_deeppot_pd.cc new file mode 100644 index 0000000000..f39aa84640 --- /dev/null +++ b/source/api_cc/tests/test_deeppot_pd.cc @@ -0,0 +1,635 @@ +// SPDX-License-Identifier: LGPL-3.0-or-later +#include +#include +#include +#include + +#include +#include +#include +#include + +#include "DeepPot.h" +#include "neighbor_list.h" +#include "test_utils.h" + +#ifdef EPSILON +#undef EPSILON +#define EPSILON (std::is_same::value ? 1e-7 : 1e-4) +#endif + +typedef testing::Types PDValueTypes; + +template +class TestInferDeepPotAPd : public ::testing::Test { + protected: + std::vector coord = {12.83, 2.56, 2.18, 12.09, 2.87, 2.74, + 00.25, 3.32, 1.68, 3.36, 3.00, 1.81, + 3.51, 2.51, 2.60, 4.27, 3.22, 1.56}; + std::vector atype = {0, 1, 1, 0, 1, 1}; + std::vector box = {13., 0., 0., 0., 13., 0., 0., 0., 13.}; + std::vector expected_e = { + + -93.016873944029, -185.923296645958, -185.927096544970, + -93.019371018039, -185.926179995548, -185.924351901852}; + std::vector expected_f = { + + 0.006277522211, -0.001117962774, 0.000618580445, 0.009928999655, + 0.003026035654, -0.006941982227, 0.000667853212, -0.002449963843, + 0.006506463508, -0.007284129115, 0.000530662205, -0.000028806821, + 0.000068097781, 0.006121331983, -0.009019754602, -0.009658343745, + -0.006110103225, 0.008865499697}; + std::vector expected_v = { + -0.000155238009, 0.000116605516, -0.007869862476, 0.000465578340, + 0.008182547185, -0.002398713212, -0.008112887338, -0.002423738425, + 0.007210716605, -0.019203504012, 0.001724938709, 0.009909211091, + 0.001153857542, -0.001600015103, -0.000560024090, 0.010727836276, + -0.001034836404, -0.007973454377, -0.021517399106, -0.004064359664, + 0.004866398692, -0.003360038617, -0.007241406162, 0.005920941051, + 0.004899151657, 0.006290788591, -0.006478820311, 0.001921504710, + 0.001313470921, -0.000304091236, 0.001684345981, 0.004124109256, + -0.006396084465, -0.000701095618, -0.006356507032, 0.009818550859, + -0.015230664587, -0.000110244376, 0.000690319396, 0.000045953023, + -0.005726548770, 0.008769818495, -0.000572380210, 0.008860603423, + -0.013819348050, -0.021227082558, -0.004977781343, 0.006646239696, + -0.005987066507, -0.002767831232, 0.003746502525, 0.007697590397, + 0.003746130152, -0.005172634748}; + int natoms; + double expected_tot_e; + std::vector expected_tot_v; + + deepmd::DeepPot dp; + + void SetUp() override { + std::string file_name = "../../tests/infer/deeppot_sea.json"; + + dp.init(file_name); + + natoms = expected_e.size(); + EXPECT_EQ(natoms * 3, expected_f.size()); + EXPECT_EQ(natoms * 9, expected_v.size()); + expected_tot_e = 0.; + expected_tot_v.resize(9); + std::fill(expected_tot_v.begin(), expected_tot_v.end(), 0.); + for (int ii = 0; ii < natoms; ++ii) { + expected_tot_e += expected_e[ii]; + } + for (int ii = 0; ii < natoms; ++ii) { + for (int dd = 0; dd < 9; ++dd) { + expected_tot_v[dd] += expected_v[ii * 9 + dd]; + } + } + }; + + void TearDown() override {}; +}; + +TYPED_TEST_SUITE(TestInferDeepPotAPd, PDValueTypes); + +TYPED_TEST(TestInferDeepPotAPd, cpu_build_nlist) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::DeepPot& dp = this->dp; + double ener; + std::vector force, virial; + dp.compute(ener, force, virial, coord, atype, box); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAPd, cpu_build_nlist_numfv) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::DeepPot& dp = this->dp; + class MyModel : public EnergyModelTest { + deepmd::DeepPot& mydp; + const std::vector atype; + + public: + MyModel(deepmd::DeepPot& dp_, const std::vector& atype_) + : mydp(dp_), atype(atype_) {}; + virtual void compute(double& ener, + std::vector& force, + std::vector& virial, + const std::vector& coord, + const std::vector& box) { + mydp.compute(ener, force, virial, coord, atype, box); + } + }; + MyModel model(dp, atype); + model.test_f(coord, box); + model.test_v(coord, box); + std::vector box_(box); + box_[1] -= 0.4; + model.test_f(coord, box_); + model.test_v(coord, box_); + box_[2] += 0.5; + model.test_f(coord, box_); + model.test_v(coord, box_); + box_[4] += 0.2; + model.test_f(coord, box_); + model.test_v(coord, box_); + box_[3] -= 0.3; + model.test_f(coord, box_); + model.test_v(coord, box_); + box_[6] -= 0.7; + model.test_f(coord, box_); + model.test_v(coord, box_); + box_[7] += 0.6; + model.test_f(coord, box_); + model.test_v(coord, box_); +} + +TYPED_TEST(TestInferDeepPotAPd, cpu_build_nlist_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::DeepPot& dp = this->dp; + double ener; + std::vector force, virial, atom_ener, atom_vir; + dp.compute(ener, force, virial, atom_ener, atom_vir, coord, atype, box); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + EXPECT_EQ(atom_ener.size(), natoms); + EXPECT_EQ(atom_vir.size(), natoms * 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAPd, cpu_lmp_nlist) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + int nloc = coord.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, coord, + atype, box, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + + double ener; + std::vector force_, virial; + dp.compute(ener, force_, virial, coord_cpy, atype_cpy, box, nall - nloc, + inlist, 0); + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + + ener = 0.; + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + dp.compute(ener, force_, virial, coord_cpy, atype_cpy, box, nall - nloc, + inlist, 1); + _fold_back(force, force_, mapping, nloc, nall, 3); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAPd, cpu_lmp_nlist_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + int nloc = coord.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, coord, + atype, box, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + double ener; + std::vector force_, atom_ener_, atom_vir_, virial; + std::vector force, atom_ener, atom_vir; + dp.compute(ener, force_, virial, atom_ener_, atom_vir_, coord_cpy, atype_cpy, + box, nall - nloc, inlist, 0); + _fold_back(force, force_, mapping, nloc, nall, 3); + _fold_back(atom_ener, atom_ener_, mapping, nloc, nall, 1); + _fold_back(atom_vir, atom_vir_, mapping, nloc, nall, 9); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + EXPECT_EQ(atom_ener.size(), natoms); + EXPECT_EQ(atom_vir.size(), natoms * 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } + + ener = 0.; + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + std::fill(atom_ener_.begin(), atom_ener_.end(), 0.0); + std::fill(atom_vir_.begin(), atom_vir_.end(), 0.0); + dp.compute(ener, force_, virial, atom_ener_, atom_vir_, coord_cpy, atype_cpy, + box, nall - nloc, inlist, 1); + _fold_back(force, force_, mapping, nloc, nall, 3); + _fold_back(atom_ener, atom_ener_, mapping, nloc, nall, 1); + _fold_back(atom_vir, atom_vir_, mapping, nloc, nall, 9); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + EXPECT_EQ(atom_ener.size(), natoms); + EXPECT_EQ(atom_vir.size(), natoms * 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + for (int ii = 0; ii < natoms; ++ii) { + EXPECT_LT(fabs(atom_ener[ii] - expected_e[ii]), EPSILON); + } + for (int ii = 0; ii < natoms * 9; ++ii) { + EXPECT_LT(fabs(atom_vir[ii] - expected_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAPd, cpu_lmp_nlist_2rc) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + int nloc = coord.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, coord, + atype, box, rc * 2); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + + double ener; + std::vector force_(nall * 3, 0.0), virial(9, 0.0); + dp.compute(ener, force_, virial, coord_cpy, atype_cpy, box, nall - nloc, + inlist, 0); + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } + + ener = 0.; + std::fill(force_.begin(), force_.end(), 0.0); + std::fill(virial.begin(), virial.end(), 0.0); + dp.compute(ener, force_, virial, coord_cpy, atype_cpy, box, nall - nloc, + inlist, 1); + _fold_back(force, force_, mapping, nloc, nall, 3); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAPd, cpu_lmp_nlist_type_sel) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + + // add vir atoms + int nvir = 2; + std::vector coord_vir(nvir * 3); + std::vector atype_vir(nvir, 2); + for (int ii = 0; ii < nvir; ++ii) { + coord_vir[ii] = coord[ii]; + } + coord.insert(coord.begin(), coord_vir.begin(), coord_vir.end()); + atype.insert(atype.begin(), atype_vir.begin(), atype_vir.end()); + natoms += nvir; + std::vector expected_f_vir(nvir * 3, 0.0); + expected_f.insert(expected_f.begin(), expected_f_vir.begin(), + expected_f_vir.end()); + + // build nlist + int nloc = coord.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, coord, + atype, box, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + + // dp compute + double ener; + std::vector force_(nall * 3, 0.0), virial(9, 0.0); + dp.compute(ener, force_, virial, coord_cpy, atype_cpy, box, nall - nloc, + inlist, 0); + // fold back + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAPd, cpu_lmp_nlist_type_sel_atomic) { + using VALUETYPE = TypeParam; + std::vector& coord = this->coord; + std::vector& atype = this->atype; + std::vector& box = this->box; + std::vector& expected_e = this->expected_e; + std::vector& expected_f = this->expected_f; + std::vector& expected_v = this->expected_v; + int& natoms = this->natoms; + double& expected_tot_e = this->expected_tot_e; + std::vector& expected_tot_v = this->expected_tot_v; + deepmd::DeepPot& dp = this->dp; + float rc = dp.cutoff(); + + // add vir atoms + int nvir = 2; + std::vector coord_vir(nvir * 3); + std::vector atype_vir(nvir, 2); + for (int ii = 0; ii < nvir; ++ii) { + coord_vir[ii] = coord[ii]; + } + coord.insert(coord.begin(), coord_vir.begin(), coord_vir.end()); + atype.insert(atype.begin(), atype_vir.begin(), atype_vir.end()); + natoms += nvir; + std::vector expected_f_vir(nvir * 3, 0.0); + expected_f.insert(expected_f.begin(), expected_f_vir.begin(), + expected_f_vir.end()); + + // build nlist + int nloc = coord.size() / 3; + std::vector coord_cpy; + std::vector atype_cpy, mapping; + std::vector > nlist_data; + _build_nlist(nlist_data, coord_cpy, atype_cpy, mapping, coord, + atype, box, rc); + int nall = coord_cpy.size() / 3; + std::vector ilist(nloc), numneigh(nloc); + std::vector firstneigh(nloc); + deepmd::InputNlist inlist(nloc, &ilist[0], &numneigh[0], &firstneigh[0]); + convert_nlist(inlist, nlist_data); + + // dp compute + double ener; + std::vector force_(nall * 3, 0.0), virial(9, 0.0), atomic_energy, + atomic_virial; + dp.compute(ener, force_, virial, atomic_energy, atomic_virial, coord_cpy, + atype_cpy, box, nall - nloc, inlist, 0); + // fold back + std::vector force; + _fold_back(force, force_, mapping, nloc, nall, 3); + + EXPECT_EQ(force.size(), natoms * 3); + EXPECT_EQ(virial.size(), 9); + + EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); + for (int ii = 0; ii < natoms * 3; ++ii) { + EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); + } + for (int ii = 0; ii < 3 * 3; ++ii) { + EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); + } +} + +TYPED_TEST(TestInferDeepPotAPd, print_summary) { + deepmd::DeepPot& dp = this->dp; + dp.print_summary(""); +} + +template +class TestInferDeepPotAPdNoPbc : public ::testing::Test { + protected: + std::vector coord = {12.83, 2.56, 2.18, 12.09, 2.87, 2.74, + 00.25, 3.32, 1.68, 3.36, 3.00, 1.81, + 3.51, 2.51, 2.60, 4.27, 3.22, 1.56}; + std::vector atype = {0, 1, 1, 0, 1, 1}; + std::vector box = + {}; // 0-size input will be supported in the future + std::vector expected_e = {-93.003304908874, -185.915806542480, + -185.928116717624, -93.017934934346, + -185.924393412278, -185.923906740801}; + std::vector expected_f = { + 0.000868182637, -0.000363698132, -0.000657003077, -0.000868182637, + 0.000363698132, 0.000657003077, 0.007932614680, -0.001003609844, + 0.000737731722, -0.003883788858, 0.000686896282, -0.000578400682, + 0.004064895086, 0.006115547962, -0.008747097814, -0.008113720908, + -0.005798834400, 0.008587766774}; + std::vector expected_v = { + 0.007762485364, -0.003251851977, -0.005874313248, -0.003251851977, + 0.001362262315, 0.002460860955, -0.005874313248, 0.002460860955, + 0.004445426242, -0.007120030212, 0.002982715359, 0.005388130971, + 0.002982715359, -0.001249515894, -0.002257190002, 0.005388130971, + -0.002257190002, -0.004077504519, -0.015805863589, 0.001952684835, + -0.001522876482, 0.001796574704, -0.000358803950, 0.000369710813, + -0.001108943040, 0.000332585300, -0.000395481309, 0.008873525623, + 0.001919112114, -0.001486235522, 0.002002929532, 0.004222469272, + -0.006517211126, -0.001656192522, -0.006501210045, 0.010118622295, + -0.006548889778, -0.000465126991, 0.001002876603, 0.000240398734, + -0.005794489784, 0.008940685179, -0.000121727685, 0.008931999051, + -0.013852797563, -0.017962955675, -0.004645050453, 0.006214692837, + -0.005278283465, -0.002662692758, 0.003618275905, 0.007095320684, + 0.003648086464, -0.005023397513}; + int natoms; + double expected_tot_e; + std::vector expected_tot_v; + + deepmd::DeepPot dp; + + void SetUp() override { + std::string file_name = "../../tests/infer/deeppot_sea.json"; + dp.init(file_name); + + natoms = expected_e.size(); + EXPECT_EQ(natoms * 3, expected_f.size()); + EXPECT_EQ(natoms * 9, expected_v.size()); + expected_tot_e = 0.; + expected_tot_v.resize(9); + std::fill(expected_tot_v.begin(), expected_tot_v.end(), 0.); + for (int ii = 0; ii < natoms; ++ii) { + expected_tot_e += expected_e[ii]; + } + for (int ii = 0; ii < natoms; ++ii) { + for (int dd = 0; dd < 9; ++dd) { + expected_tot_v[dd] += expected_v[ii * 9 + dd]; + } + } + }; + + void TearDown() override { remove("deeppot.pb"); }; +}; + +/* +NOTE: Disable TestInferDeepPotAPdNoPbc cuerrently for box is a 0-size Tensor, +which is not fully supported in paddle yet +*/ +// TYPED_TEST_SUITE(TestInferDeepPotAPdNoPbc, PDValueTypes); + +// TYPED_TEST(TestInferDeepPotAPdNoPbc, cpu_build_nlist) { +// using VALUETYPE = TypeParam; +// std::vector& coord = this->coord; +// std::vector& atype = this->atype; +// std::vector& box = this->box; +// std::vector& expected_e = this->expected_e; +// std::vector& expected_f = this->expected_f; +// std::vector& expected_v = this->expected_v; +// int& natoms = this->natoms; +// double& expected_tot_e = this->expected_tot_e; +// std::vector& expected_tot_v = this->expected_tot_v; +// deepmd::DeepPot& dp = this->dp; +// double ener; +// std::vector force, virial; +// dp.compute(ener, force, virial, coord, atype, box); + +// EXPECT_EQ(force.size(), natoms * 3); +// EXPECT_EQ(virial.size(), 9); + +// EXPECT_LT(fabs(ener - expected_tot_e), EPSILON); +// for (int ii = 0; ii < natoms * 3; ++ii) { +// EXPECT_LT(fabs(force[ii] - expected_f[ii]), EPSILON); +// } +// for (int ii = 0; ii < 3 * 3; ++ii) { +// EXPECT_LT(fabs(virial[ii] - expected_tot_v[ii]), EPSILON); +// } +// } diff --git a/source/config/CMakeLists.txt b/source/config/CMakeLists.txt index b1ce17566f..dd005a327b 100644 --- a/source/config/CMakeLists.txt +++ b/source/config/CMakeLists.txt @@ -14,6 +14,12 @@ else() set(ENABLE_PYTORCH 0) endif() +if(ENABLE_PADDLE) + set(ENABLE_PADDLE 1) +else() + set(ENABLE_PADDLE 0) +endif() + configure_file("run_config.ini" "${CMAKE_CURRENT_BINARY_DIR}/run_config.ini" @ONLY) diff --git a/source/config/run_config.ini b/source/config/run_config.ini index fb96ad224e..7bb6041af9 100644 --- a/source/config/run_config.ini +++ b/source/config/run_config.ini @@ -14,5 +14,7 @@ PT_INCLUDE_DIR = @TORCH_INCLUDE_DIRS@ PT_LIBS = @PyTorch_LIBRARY_PATH@ PT_VERSIOn = @Torch_VERSION@ PT_CXX11_ABI_FLAG = @OP_CXX_ABI_PT@ +PD_VERSION = @PADDLE_VERSION@ +PD_INFERENCE_DIR = @PADDLE_INFERENCE_DIR@ MODEL_VERSION=@MODEL_VERSION@ DP_VARIANT=@DP_VARIANT@ diff --git a/source/install/test_cc_local.sh b/source/install/test_cc_local.sh index 8ce4de4b21..af142aaa2e 100755 --- a/source/install/test_cc_local.sh +++ b/source/install/test_cc_local.sh @@ -1,4 +1,4 @@ -set -e +set -ex if [ "$DP_VARIANT" = "cuda" ]; then CUDA_ARGS="-DUSE_CUDA_TOOLKIT=TRUE" @@ -16,11 +16,13 @@ NPROC=$(nproc --all) echo "try to find tensorflow in the Python environment" INSTALL_PREFIX=${SCRIPT_PATH}/../../dp_test BUILD_TMP_DIR=${SCRIPT_PATH}/../build_tests +PADDLE_INFERENCE_DIR=${BUILD_TMP_DIR}/paddle_inference_install_dir mkdir -p ${BUILD_TMP_DIR} cd ${BUILD_TMP_DIR} cmake \ -D ENABLE_TENSORFLOW=TRUE \ -D ENABLE_PYTORCH=TRUE \ + -D ENABLE_PADDLE=TRUE \ -D INSTALL_TENSORFLOW=FALSE \ -D USE_TF_PYTHON_LIBS=TRUE \ -D CMAKE_INSTALL_PREFIX=${INSTALL_PREFIX} \ @@ -29,4 +31,6 @@ cmake \ ${CUDA_ARGS} .. cmake --build . -j${NPROC} cmake --install . +PADDLE_INFERENCE_DIR=${BUILD_TMP_DIR}/paddle_inference_install_dir +export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${PADDLE_INFERENCE_DIR}/third_party/install/onednn/lib:${PADDLE_INFERENCE_DIR}/third_party/install/mklml/lib ctest --output-on-failure diff --git a/source/lib/include/ewald.h b/source/lib/include/ewald.h index 130384650b..659ca90565 100644 --- a/source/lib/include/ewald.h +++ b/source/lib/include/ewald.h @@ -6,8 +6,8 @@ #if defined(_OPENMP) #include #else -int omp_get_num_threads() { return 1; } -int omp_get_thread_num() { return 0; } +inline int omp_get_num_threads() { return 1; } +inline int omp_get_thread_num() { return 0; } #endif #include "region.h" diff --git a/source/lmp/plugin/CMakeLists.txt b/source/lmp/plugin/CMakeLists.txt index 13da3d7114..1e5afb4444 100644 --- a/source/lmp/plugin/CMakeLists.txt +++ b/source/lmp/plugin/CMakeLists.txt @@ -8,6 +8,7 @@ if(DEFINED LAMMPS_SOURCE_ROOT OR DEFINED LAMMPS_VERSION) lammps_download GIT_REPOSITORY https://github.com/lammps/lammps GIT_TAG ${LAMMPS_VERSION}) + message(STATUS "STARTING DOWNLOAD LAMMPS TO: " ${LAMMPS_SOURCE_ROOT}) FetchContent_MakeAvailable(lammps_download) set(LAMMPS_SOURCE_ROOT ${lammps_download_SOURCE_DIR}) endif() diff --git a/source/lmp/tests/test_lammps_pd.py b/source/lmp/tests/test_lammps_pd.py new file mode 100644 index 0000000000..7824fa5b0f --- /dev/null +++ b/source/lmp/tests/test_lammps_pd.py @@ -0,0 +1,753 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +import importlib +import os +import shutil +import subprocess as sp +import sys +import tempfile +from pathlib import ( + Path, +) + +import constants +import numpy as np +import pytest +from lammps import ( + PyLammps, +) +from write_lmp_data import ( + write_lmp_data, +) + +pbtxt_file2 = ( + Path(__file__).parent.parent.parent / "tests" / "infer" / "deeppot-1.pbtxt" +) +pb_file = Path(__file__).parent.parent.parent / "tests" / "infer" / "deeppot_sea.json" +pb_file2 = Path(__file__).parent / "graph2.pb" +system_file = Path(__file__).parent.parent.parent / "tests" +data_file = Path(__file__).parent / "data.lmp" +data_file_si = Path(__file__).parent / "data.si" +data_type_map_file = Path(__file__).parent / "data_type_map.lmp" +md_file = Path(__file__).parent / "md.out" + +ATOL = 1e-7 +RTOL = 1e-6 + +# this is as the same as python and c++ tests, test_deeppot_a.py +expected_ae = np.array( + [ + -93.016873944029, + -185.923296645958, + -185.927096544970, + -93.019371018039, + -185.926179995548, + -185.924351901852, + ] +) +expected_e = np.sum(expected_ae) +expected_f = np.array( + [ + 0.006277522211, + -0.001117962774, + 0.000618580445, + 0.009928999655, + 0.003026035654, + -0.006941982227, + 0.000667853212, + -0.002449963843, + 0.006506463508, + -0.007284129115, + 0.000530662205, + -0.000028806821, + 0.000068097781, + 0.006121331983, + -0.009019754602, + -0.009658343745, + -0.006110103225, + 0.008865499697, + ] +).reshape(6, 3) + +expected_f2 = np.array( + [ + [-0.6454949, 1.72457783, 0.18897958], + [1.68936514, -0.36995299, -1.36044464], + [-1.09902692, -1.35487928, 1.17416702], + [1.68426111, -0.50835585, 0.98340415], + [0.05771758, 1.12515818, -1.77561531], + [-1.686822, -0.61654789, 0.78950921], + ] +) + +expected_v = -np.array( + [ + -0.000155238009, + 0.000116605516, + -0.007869862476, + 0.000465578340, + 0.008182547185, + -0.002398713212, + -0.008112887338, + -0.002423738425, + 0.007210716605, + -0.019203504012, + 0.001724938709, + 0.009909211091, + 0.001153857542, + -0.001600015103, + -0.000560024090, + 0.010727836276, + -0.001034836404, + -0.007973454377, + -0.021517399106, + -0.004064359664, + 0.004866398692, + -0.003360038617, + -0.007241406162, + 0.005920941051, + 0.004899151657, + 0.006290788591, + -0.006478820311, + 0.001921504710, + 0.001313470921, + -0.000304091236, + 0.001684345981, + 0.004124109256, + -0.006396084465, + -0.000701095618, + -0.006356507032, + 0.009818550859, + -0.015230664587, + -0.000110244376, + 0.000690319396, + 0.000045953023, + -0.005726548770, + 0.008769818495, + -0.000572380210, + 0.008860603423, + -0.013819348050, + -0.021227082558, + -0.004977781343, + 0.006646239696, + -0.005987066507, + -0.002767831232, + 0.003746502525, + 0.007697590397, + 0.003746130152, + -0.005172634748, + ] +).reshape(6, 9) +expected_v2 = -np.array( + [ + [ + -0.70008436, + -0.06399891, + 0.63678391, + -0.07642171, + -0.70580035, + 0.20506145, + 0.64098364, + 0.20305781, + -0.57906794, + ], + [ + -0.6372635, + 0.14315552, + 0.51952246, + 0.04604049, + -0.06003681, + -0.02688702, + 0.54489318, + -0.10951559, + -0.43730539, + ], + [ + -0.25090748, + -0.37466262, + 0.34085833, + -0.26690852, + -0.37676917, + 0.29080825, + 0.31600481, + 0.37558276, + -0.33251064, + ], + [ + -0.80195614, + -0.10273138, + 0.06935364, + -0.10429256, + -0.29693811, + 0.45643496, + 0.07247872, + 0.45604679, + -0.71048816, + ], + [ + -0.03840668, + -0.07680205, + 0.10940472, + -0.02374189, + -0.27610266, + 0.4336071, + 0.02465248, + 0.4290638, + -0.67496763, + ], + [ + -0.61475065, + -0.21163135, + 0.26652929, + -0.26134659, + -0.11560267, + 0.15415902, + 0.34343952, + 0.1589482, + -0.21370642, + ], + ] +).reshape(6, 9) + +box = np.array([0, 13, 0, 13, 0, 13, 0, 0, 0]) +coord = np.array( + [ + [12.83, 2.56, 2.18], + [12.09, 2.87, 2.74], + [0.25, 3.32, 1.68], + [3.36, 3.00, 1.81], + [3.51, 2.51, 2.60], + [4.27, 3.22, 1.56], + ] +) +type_OH = np.array([1, 2, 2, 1, 2, 2]) +type_HO = np.array([2, 1, 1, 2, 1, 1]) + + +sp.check_output( + f"{sys.executable} -m deepmd convert-from pbtxt -i {pbtxt_file2.resolve()} -o {pb_file2.resolve()}".split() +) + + +def setup_module(): + write_lmp_data(box, coord, type_OH, data_file) + write_lmp_data(box, coord, type_HO, data_type_map_file) + write_lmp_data( + box * constants.dist_metal2si, + coord * constants.dist_metal2si, + type_OH, + data_file_si, + ) + + +def teardown_module(): + os.remove(data_file) + os.remove(data_type_map_file) + + +def _lammps(data_file, units="metal") -> PyLammps: + lammps = PyLammps() + lammps.units(units) + lammps.boundary("p p p") + lammps.atom_style("atomic") + if units == "metal" or units == "real": + lammps.neighbor("2.0 bin") + elif units == "si": + lammps.neighbor("2.0e-10 bin") + else: + raise ValueError("units should be metal, real, or si") + lammps.neigh_modify("every 10 delay 0 check no") + lammps.read_data(data_file.resolve()) + if units == "metal" or units == "real": + lammps.mass("1 16") + lammps.mass("2 2") + elif units == "si": + lammps.mass("1 %.10e" % (16 * constants.mass_metal2si)) + lammps.mass("2 %.10e" % (2 * constants.mass_metal2si)) + else: + raise ValueError("units should be metal, real, or si") + if units == "metal": + lammps.timestep(0.0005) + elif units == "real": + lammps.timestep(0.5) + elif units == "si": + lammps.timestep(5e-16) + else: + raise ValueError("units should be metal, real, or si") + lammps.fix("1 all nve") + return lammps + + +@pytest.fixture +def lammps(): + lmp = _lammps(data_file=data_file) + yield lmp + lmp.close() + + +@pytest.fixture +def lammps_type_map(): + lmp = _lammps(data_file=data_type_map_file) + yield lmp + lmp.close() + + +@pytest.fixture +def lammps_real(): + lmp = _lammps(data_file=data_file, units="real") + yield lmp + lmp.close() + + +@pytest.fixture +def lammps_si(): + lmp = _lammps(data_file=data_file_si, units="si") + yield lmp + lmp.close() + + +def test_pair_deepmd(lammps): + lammps.pair_style(f"deepmd {pb_file.resolve()}") + lammps.pair_coeff("* *") + lammps.run(0) + assert lammps.eval("pe") == pytest.approx(expected_e) + for ii in range(6): + assert lammps.atoms[ii].force == pytest.approx( + expected_f[lammps.atoms[ii].id - 1] + ) + lammps.run(1) + + +def test_pair_deepmd_virial(lammps): + lammps.pair_style(f"deepmd {pb_file.resolve()}") + lammps.pair_coeff("* *") + lammps.compute("virial all centroid/stress/atom NULL pair") + for ii in range(9): + jj = [0, 4, 8, 3, 6, 7, 1, 2, 5][ii] + lammps.variable(f"virial{jj} atom c_virial[{ii+1}]") + lammps.dump( + "1 all custom 1 dump id " + " ".join([f"v_virial{ii}" for ii in range(9)]) + ) + lammps.run(0) + assert lammps.eval("pe") == pytest.approx(expected_e) + for ii in range(6): + assert lammps.atoms[ii].force == pytest.approx( + expected_f[lammps.atoms[ii].id - 1] + ) + idx_map = lammps.lmp.numpy.extract_atom("id") - 1 + for ii in range(9): + assert np.array( + lammps.variables[f"virial{ii}"].value + ) / constants.nktv2p == pytest.approx(expected_v[idx_map, ii], RTOL, ATOL) + + +def test_pair_deepmd_model_devi(lammps): + lammps.pair_style( + f"deepmd {pb_file.resolve()} {pb_file2.resolve()} out_file {md_file.resolve()} out_freq 1 atomic" + ) + lammps.pair_coeff("* *") + lammps.run(0) + assert lammps.eval("pe") == pytest.approx(expected_e) + for ii in range(6): + assert lammps.atoms[ii].force == pytest.approx( + expected_f[lammps.atoms[ii].id - 1] + ) + # load model devi + md = np.loadtxt(md_file.resolve()) + expected_md_f = np.linalg.norm(np.std([expected_f, expected_f2], axis=0), axis=1) + assert md[7:] == pytest.approx(expected_md_f) + assert md[4] == pytest.approx(np.max(expected_md_f)) + assert md[5] == pytest.approx(np.min(expected_md_f)) + assert md[6] == pytest.approx(np.mean(expected_md_f)) + expected_md_v = ( + np.std([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) / 6 + ) + assert md[1] == pytest.approx(np.max(expected_md_v)) + assert md[2] == pytest.approx(np.min(expected_md_v)) + assert md[3] == pytest.approx(np.sqrt(np.mean(np.square(expected_md_v)))) + + +def test_pair_deepmd_model_devi_virial(lammps): + lammps.pair_style( + f"deepmd {pb_file.resolve()} {pb_file2.resolve()} out_file {md_file.resolve()} out_freq 1 atomic" + ) + lammps.pair_coeff("* *") + lammps.compute("virial all centroid/stress/atom NULL pair") + for ii in range(9): + jj = [0, 4, 8, 3, 6, 7, 1, 2, 5][ii] + lammps.variable(f"virial{jj} atom c_virial[{ii+1}]") + lammps.dump( + "1 all custom 1 dump id " + " ".join([f"v_virial{ii}" for ii in range(9)]) + ) + lammps.run(0) + assert lammps.eval("pe") == pytest.approx(expected_e, RTOL, ATOL) + for ii in range(6): + assert lammps.atoms[ii].force == pytest.approx( + expected_f[lammps.atoms[ii].id - 1], RTOL, ATOL + ) + idx_map = lammps.lmp.numpy.extract_atom("id") - 1 + for ii in range(9): + assert np.array( + lammps.variables[f"virial{ii}"].value + ) / constants.nktv2p == pytest.approx(expected_v[idx_map, ii], RTOL, ATOL) + # load model devi + md = np.loadtxt(md_file.resolve()) + expected_md_f = np.linalg.norm(np.std([expected_f, expected_f2], axis=0), axis=1) + assert md[7:] == pytest.approx(expected_md_f, RTOL, ATOL) + assert md[4] == pytest.approx(np.max(expected_md_f), RTOL, ATOL) + assert md[5] == pytest.approx(np.min(expected_md_f), RTOL, ATOL) + assert md[6] == pytest.approx(np.mean(expected_md_f), RTOL, ATOL) + expected_md_v = ( + np.std([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) / 6 + ) + assert md[1] == pytest.approx(np.max(expected_md_v), RTOL, ATOL) + assert md[2] == pytest.approx(np.min(expected_md_v), RTOL, ATOL) + assert md[3] == pytest.approx( + np.sqrt(np.mean(np.square(expected_md_v))), RTOL, ATOL + ) + + +def test_pair_deepmd_model_devi_atomic_relative(lammps): + relative = 1.0 + lammps.pair_style( + f"deepmd {pb_file.resolve()} {pb_file2.resolve()} out_file {md_file.resolve()} out_freq 1 atomic relative {relative}" + ) + lammps.pair_coeff("* *") + lammps.run(0) + assert lammps.eval("pe") == pytest.approx(expected_e) + for ii in range(6): + assert lammps.atoms[ii].force == pytest.approx( + expected_f[lammps.atoms[ii].id - 1] + ) + # load model devi + md = np.loadtxt(md_file.resolve()) + norm = np.linalg.norm(np.mean([expected_f, expected_f2], axis=0), axis=1) + expected_md_f = np.linalg.norm(np.std([expected_f, expected_f2], axis=0), axis=1) + expected_md_f /= norm + relative + assert md[7:] == pytest.approx(expected_md_f) + assert md[4] == pytest.approx(np.max(expected_md_f)) + assert md[5] == pytest.approx(np.min(expected_md_f)) + assert md[6] == pytest.approx(np.mean(expected_md_f)) + expected_md_v = ( + np.std([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) / 6 + ) + assert md[1] == pytest.approx(np.max(expected_md_v)) + assert md[2] == pytest.approx(np.min(expected_md_v)) + assert md[3] == pytest.approx(np.sqrt(np.mean(np.square(expected_md_v)))) + + +def test_pair_deepmd_model_devi_atomic_relative_v(lammps): + relative = 1.0 + lammps.pair_style( + f"deepmd {pb_file.resolve()} {pb_file2.resolve()} out_file {md_file.resolve()} out_freq 1 atomic relative_v {relative}" + ) + lammps.pair_coeff("* *") + lammps.run(0) + assert lammps.eval("pe") == pytest.approx(expected_e) + for ii in range(6): + assert lammps.atoms[ii].force == pytest.approx( + expected_f[lammps.atoms[ii].id - 1] + ) + md = np.loadtxt(md_file.resolve()) + expected_md_f = np.linalg.norm(np.std([expected_f, expected_f2], axis=0), axis=1) + assert md[7:] == pytest.approx(expected_md_f) + assert md[4] == pytest.approx(np.max(expected_md_f)) + assert md[5] == pytest.approx(np.min(expected_md_f)) + assert md[6] == pytest.approx(np.mean(expected_md_f)) + expected_md_v = ( + np.std([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) / 6 + ) + norm = ( + np.abs( + np.mean([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) + ) + / 6 + ) + expected_md_v /= norm + relative + assert md[1] == pytest.approx(np.max(expected_md_v)) + assert md[2] == pytest.approx(np.min(expected_md_v)) + assert md[3] == pytest.approx(np.sqrt(np.mean(np.square(expected_md_v)))) + + +def test_pair_deepmd_type_map(lammps_type_map): + lammps_type_map.pair_style(f"deepmd {pb_file.resolve()}") + lammps_type_map.pair_coeff("* * H O") + lammps_type_map.run(0) + assert lammps_type_map.eval("pe") == pytest.approx(expected_e) + for ii in range(6): + assert lammps_type_map.atoms[ii].force == pytest.approx( + expected_f[lammps_type_map.atoms[ii].id - 1] + ) + lammps_type_map.run(1) + + +def test_pair_deepmd_real(lammps_real): + lammps_real.pair_style(f"deepmd {pb_file.resolve()}") + lammps_real.pair_coeff("* *") + lammps_real.run(0) + assert lammps_real.eval("pe") == pytest.approx( + expected_e * constants.ener_metal2real + ) + for ii in range(6): + assert lammps_real.atoms[ii].force == pytest.approx( + expected_f[lammps_real.atoms[ii].id - 1] * constants.force_metal2real + ) + lammps_real.run(1) + + +def test_pair_deepmd_virial_real(lammps_real): + lammps_real.pair_style(f"deepmd {pb_file.resolve()}") + lammps_real.pair_coeff("* *") + lammps_real.compute("virial all centroid/stress/atom NULL pair") + for ii in range(9): + jj = [0, 4, 8, 3, 6, 7, 1, 2, 5][ii] + lammps_real.variable(f"virial{jj} atom c_virial[{ii+1}]") + lammps_real.dump( + "1 all custom 1 dump id " + " ".join([f"v_virial{ii}" for ii in range(9)]) + ) + lammps_real.run(0) + assert lammps_real.eval("pe") == pytest.approx( + expected_e * constants.ener_metal2real, + RTOL, + ATOL, + ) + for ii in range(6): + assert lammps_real.atoms[ii].force == pytest.approx( + expected_f[lammps_real.atoms[ii].id - 1] * constants.force_metal2real, + RTOL, + ATOL, + ) + idx_map = lammps_real.lmp.numpy.extract_atom("id") - 1 + for ii in range(9): + assert np.array( + lammps_real.variables[f"virial{ii}"].value + ) / constants.nktv2p_real == pytest.approx( + expected_v[idx_map, ii] * constants.ener_metal2real, + RTOL, + ATOL, + ) + + +def test_pair_deepmd_model_devi_real(lammps_real): + lammps_real.pair_style( + f"deepmd {pb_file.resolve()} {pb_file2.resolve()} out_file {md_file.resolve()} out_freq 1 atomic" + ) + lammps_real.pair_coeff("* *") + lammps_real.run(0) + assert lammps_real.eval("pe") == pytest.approx( + expected_e * constants.ener_metal2real + ) + for ii in range(6): + assert lammps_real.atoms[ii].force == pytest.approx( + expected_f[lammps_real.atoms[ii].id - 1] * constants.force_metal2real + ) + # load model devi + md = np.loadtxt(md_file.resolve()) + expected_md_f = np.linalg.norm(np.std([expected_f, expected_f2], axis=0), axis=1) + assert md[7:] == pytest.approx(expected_md_f * constants.force_metal2real) + assert md[4] == pytest.approx(np.max(expected_md_f) * constants.force_metal2real) + assert md[5] == pytest.approx(np.min(expected_md_f) * constants.force_metal2real) + assert md[6] == pytest.approx(np.mean(expected_md_f) * constants.force_metal2real) + expected_md_v = ( + np.std([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) / 6 + ) + assert md[1] == pytest.approx(np.max(expected_md_v) * constants.ener_metal2real) + assert md[2] == pytest.approx(np.min(expected_md_v) * constants.ener_metal2real) + assert md[3] == pytest.approx( + np.sqrt(np.mean(np.square(expected_md_v))) * constants.ener_metal2real + ) + + +def test_pair_deepmd_model_devi_virial_real(lammps_real): + lammps_real.pair_style( + f"deepmd {pb_file.resolve()} {pb_file2.resolve()} out_file {md_file.resolve()} out_freq 1 atomic" + ) + lammps_real.pair_coeff("* *") + lammps_real.compute("virial all centroid/stress/atom NULL pair") + for ii in range(9): + jj = [0, 4, 8, 3, 6, 7, 1, 2, 5][ii] + lammps_real.variable(f"virial{jj} atom c_virial[{ii+1}]") + lammps_real.dump( + "1 all custom 1 dump id " + " ".join([f"v_virial{ii}" for ii in range(9)]) + ) + lammps_real.run(0) + assert lammps_real.eval("pe") == pytest.approx( + expected_e * constants.ener_metal2real, + RTOL, + ATOL, + ) + for ii in range(6): + assert lammps_real.atoms[ii].force == pytest.approx( + expected_f[lammps_real.atoms[ii].id - 1] * constants.force_metal2real, + RTOL, + ATOL, + ) + idx_map = lammps_real.lmp.numpy.extract_atom("id") - 1 + for ii in range(9): + assert np.array( + lammps_real.variables[f"virial{ii}"].value + ) / constants.nktv2p_real == pytest.approx( + expected_v[idx_map, ii] * constants.ener_metal2real, + RTOL, + ATOL, + ) + # load model devi + md = np.loadtxt(md_file.resolve()) + expected_md_f = np.linalg.norm(np.std([expected_f, expected_f2], axis=0), axis=1) + assert md[7:] == pytest.approx( + expected_md_f * constants.force_metal2real, RTOL, ATOL + ) + assert md[4] == pytest.approx( + np.max(expected_md_f) * constants.force_metal2real, RTOL, ATOL + ) + assert md[5] == pytest.approx( + np.min(expected_md_f) * constants.force_metal2real, RTOL, ATOL + ) + assert md[6] == pytest.approx( + np.mean(expected_md_f) * constants.force_metal2real, RTOL, ATOL + ) + expected_md_v = ( + np.std([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) / 6 + ) + assert md[1] == pytest.approx( + np.max(expected_md_v) * constants.ener_metal2real, RTOL, ATOL + ) + assert md[2] == pytest.approx( + np.min(expected_md_v) * constants.ener_metal2real, RTOL, ATOL + ) + assert md[3] == pytest.approx( + np.sqrt(np.mean(np.square(expected_md_v))) * constants.ener_metal2real, + RTOL, + ATOL, + ) + + +def test_pair_deepmd_model_devi_atomic_relative_real(lammps_real): + relative = 1.0 + lammps_real.pair_style( + f"deepmd {pb_file.resolve()} {pb_file2.resolve()} out_file {md_file.resolve()} out_freq 1 atomic relative {relative * constants.force_metal2real}" + ) + lammps_real.pair_coeff("* *") + lammps_real.run(0) + assert lammps_real.eval("pe") == pytest.approx( + expected_e * constants.ener_metal2real + ) + for ii in range(6): + assert lammps_real.atoms[ii].force == pytest.approx( + expected_f[lammps_real.atoms[ii].id - 1] * constants.force_metal2real + ) + # load model devi + md = np.loadtxt(md_file.resolve()) + norm = np.linalg.norm(np.mean([expected_f, expected_f2], axis=0), axis=1) + expected_md_f = np.linalg.norm(np.std([expected_f, expected_f2], axis=0), axis=1) + expected_md_f /= norm + relative + assert md[7:] == pytest.approx(expected_md_f * constants.force_metal2real) + assert md[4] == pytest.approx(np.max(expected_md_f) * constants.force_metal2real) + assert md[5] == pytest.approx(np.min(expected_md_f) * constants.force_metal2real) + assert md[6] == pytest.approx(np.mean(expected_md_f) * constants.force_metal2real) + expected_md_v = ( + np.std([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) / 6 + ) + assert md[1] == pytest.approx(np.max(expected_md_v) * constants.ener_metal2real) + assert md[2] == pytest.approx(np.min(expected_md_v) * constants.ener_metal2real) + assert md[3] == pytest.approx( + np.sqrt(np.mean(np.square(expected_md_v))) * constants.ener_metal2real + ) + + +def test_pair_deepmd_model_devi_atomic_relative_v_real(lammps_real): + relative = 1.0 + lammps_real.pair_style( + f"deepmd {pb_file.resolve()} {pb_file2.resolve()} out_file {md_file.resolve()} out_freq 1 atomic relative_v {relative * constants.ener_metal2real}" + ) + lammps_real.pair_coeff("* *") + lammps_real.run(0) + assert lammps_real.eval("pe") == pytest.approx( + expected_e * constants.ener_metal2real + ) + for ii in range(6): + assert lammps_real.atoms[ii].force == pytest.approx( + expected_f[lammps_real.atoms[ii].id - 1] * constants.force_metal2real + ) + md = np.loadtxt(md_file.resolve()) + expected_md_f = np.linalg.norm(np.std([expected_f, expected_f2], axis=0), axis=1) + assert md[7:] == pytest.approx(expected_md_f * constants.force_metal2real) + assert md[4] == pytest.approx(np.max(expected_md_f) * constants.force_metal2real) + assert md[5] == pytest.approx(np.min(expected_md_f) * constants.force_metal2real) + assert md[6] == pytest.approx(np.mean(expected_md_f) * constants.force_metal2real) + expected_md_v = ( + np.std([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) / 6 + ) + norm = ( + np.abs( + np.mean([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) + ) + / 6 + ) + expected_md_v /= norm + relative + assert md[1] == pytest.approx(np.max(expected_md_v) * constants.ener_metal2real) + assert md[2] == pytest.approx(np.min(expected_md_v) * constants.ener_metal2real) + assert md[3] == pytest.approx( + np.sqrt(np.mean(np.square(expected_md_v))) * constants.ener_metal2real + ) + + +def test_pair_deepmd_si(lammps_si): + lammps_si.pair_style(f"deepmd {pb_file.resolve()}") + lammps_si.pair_coeff("* *") + lammps_si.run(0) + assert lammps_si.eval("pe") == pytest.approx(expected_e * constants.ener_metal2si) + for ii in range(6): + assert lammps_si.atoms[ii].force == pytest.approx( + expected_f[lammps_si.atoms[ii].id - 1] * constants.force_metal2si + ) + lammps_si.run(1) + + +@pytest.mark.skip("skip mpi run for python paddle backend") +@pytest.mark.skipif( + shutil.which("mpirun") is None, reason="MPI is not installed on this system" +) +@pytest.mark.skipif( + importlib.util.find_spec("mpi4py") is None, reason="mpi4py is not installed" +) +@pytest.mark.parametrize( + ("balance_args",), + [(["--balance"],), ([],)], +) +def test_pair_deepmd_mpi(balance_args: list): + with tempfile.NamedTemporaryFile() as f: + sp.check_call( + [ + "mpirun", + "-n", + "2", + sys.executable, + Path(__file__).parent / "run_mpi_pair_deepmd.py", + data_file, + pb_file, + pb_file2, + md_file, + f.name, + *balance_args, + ] + ) + arr = np.loadtxt(f.name, ndmin=1) + pe = arr[0] + + relative = 1.0 + assert pe == pytest.approx(expected_e) + # load model devi + md = np.loadtxt(md_file.resolve()) + norm = np.linalg.norm(np.mean([expected_f, expected_f2], axis=0), axis=1) + expected_md_f = np.linalg.norm(np.std([expected_f, expected_f2], axis=0), axis=1) + expected_md_f /= norm + relative + assert md[7:] == pytest.approx(expected_md_f) + assert md[4] == pytest.approx(np.max(expected_md_f)) + assert md[5] == pytest.approx(np.min(expected_md_f)) + assert md[6] == pytest.approx(np.mean(expected_md_f)) + expected_md_v = ( + np.std([np.sum(expected_v, axis=0), np.sum(expected_v2, axis=0)], axis=0) / 6 + ) + assert md[1] == pytest.approx(np.max(expected_md_v)) + assert md[2] == pytest.approx(np.min(expected_md_v)) + assert md[3] == pytest.approx(np.sqrt(np.mean(np.square(expected_md_v)))) diff --git a/source/tests/infer/deeppot_sea.forward_lower.json b/source/tests/infer/deeppot_sea.forward_lower.json new file mode 100644 index 0000000000..ceafbfd70d --- /dev/null +++ b/source/tests/infer/deeppot_sea.forward_lower.json @@ -0,0 +1 @@ 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