diff --git a/deepmd/pd/utils/serialization.py b/deepmd/pd/utils/serialization.py index 79f8f7e636..e7b7cd10cd 100644 --- a/deepmd/pd/utils/serialization.py +++ b/deepmd/pd/utils/serialization.py @@ -56,16 +56,48 @@ def deserialize_to_file(model_file: str, data: dict) -> None: InputSpec, ) - jit_model = paddle.jit.to_static( - model, + """ 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="int64", name="atype"), + InputSpec([1, -1, -1], dtype="int32", name="nlist"), + None, + None, + None, + True, + None, ], ) paddle.jit.save( - jit_model, + model, model_file.split(".json")[0], ) diff --git a/source/api_cc/include/DeepPotPD.h b/source/api_cc/include/DeepPotPD.h index 7f4773c781..ec43300ca0 100644 --- a/source/api_cc/include/DeepPotPD.h +++ b/source/api_cc/include/DeepPotPD.h @@ -377,8 +377,13 @@ class DeepPotPD : public DeepPotBackend { 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; diff --git a/source/api_cc/src/DeepPotPD.cc b/source/api_cc/src/DeepPotPD.cc index 3a90cad82e..88ebf3add8 100644 --- a/source/api_cc/src/DeepPotPD.cc +++ b/source/api_cc/src/DeepPotPD.cc @@ -60,14 +60,43 @@ void DeepPotPD::init(const std::string& model, 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; - std::string pdiparams_path; + 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; @@ -77,17 +106,17 @@ void DeepPotPD::init(const std::string& model, 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(); - std::cout << "load model from: " << model << " to cpu " << std::endl; + config_fl->DisableGpu(); } else { - config->EnableUseGpu( - 4096, 0); // annotate it if use cpu, default use gpu with 4G mem - std::cout << "load model from: " << model << " to gpu:" << gpu_id - << std::endl; + config->EnableUseGpu(4096, 0); + config_fl->EnableUseGpu(4096, 0); } // NOTE: Both set to 1 now. @@ -98,9 +127,11 @@ void DeepPotPD::init(const std::string& model, 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; @@ -141,11 +172,11 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, int nloc = nall_real - nghost_real; int nframes = 1; std::vector coord_wrapped = dcoord; - auto coord_wrapped_Tensor = predictor->GetInputHandle("coord"); + 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->GetInputHandle("atype"); + auto atype_Tensor = predictor_fl->GetInputHandle("atype"); atype_Tensor->Reshape({1, nall_real}); atype_Tensor->CopyFromCpu(datype.data()); @@ -156,29 +187,30 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, 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->GetInputHandle("sendproc"); - sendproc_tensor->Reshape({nswap}); - sendproc_tensor->CopyFromCpu(lmp_list.sendproc); - auto recvproc_tensor = predictor->GetInputHandle("recvproc"); - recvproc_tensor->Reshape({nswap}); - recvproc_tensor->CopyFromCpu(lmp_list.recvproc); - auto firstrecv_tensor = predictor->GetInputHandle("firstrecv"); - firstrecv_tensor->Reshape({nswap}); - firstrecv_tensor->CopyFromCpu(lmp_list.firstrecv); - auto recvnum_tensor = predictor->GetInputHandle("recvnum"); - recvnum_tensor->Reshape({nswap}); - recvnum_tensor->CopyFromCpu(lmp_list.recvnum); - auto sendnum_tensor = predictor->GetInputHandle("sendnum"); - sendnum_tensor->Reshape({nswap}); - sendnum_tensor->CopyFromCpu(lmp_list.sendnum); - auto communicator_tensor = predictor->GetInputHandle("communicator"); - communicator_tensor->Reshape({1}); - communicator_tensor->CopyFromCpu(static_cast(lmp_list.world)); - auto sendlist_tensor = predictor->GetInputHandle("sendlist"); - - int total_send = - std::accumulate(lmp_list.sendnum, lmp_list.sendnum + nswap, 0); + // 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( @@ -186,46 +218,35 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, } } std::vector firstneigh = createNlistTensorPD(nlist_data.jlist); - firstneigh_tensor = predictor->GetInputHandle("nlist"); + 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()) { - throw deepmd::deepmd_exception("fparam is not supported as input yet."); - // fparam_tensor = predictor->GetInputHandle("fparam"); - // fparam_tensor->Reshape({1, static_cast(fparam.size())}); - // fparam_tensor->CopyFromCpu((fparam.data())); + 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()) { - throw deepmd::deepmd_exception("aparam is not supported as input yet."); - // aparam_tensor = predictor->GetInputHandle("aparam"); - // aparam_tensor->Reshape({1, lmp_list.inum, - // static_cast(aparam_.size()) / lmp_list.inum}); - // aparam_tensor->CopyFromCpu((aparam_.data())); + 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->Run()) { + if (!predictor_fl->Run()) { throw deepmd::deepmd_exception("Paddle inference run failed"); } - auto output_names = predictor->GetOutputNames(); - - auto energy_ = predictor->GetOutputHandle(output_names[1]); - auto force_ = predictor->GetOutputHandle(output_names[2]); - auto virial_ = predictor->GetOutputHandle(output_names[3]); - std::vector output_energy_shape = energy_->shape(); - int output_energy_size = - std::accumulate(output_energy_shape.begin(), output_energy_shape.end(), 1, - std::multiplies()); - std::vector output_force_shape = force_->shape(); - int output_force_size = - std::accumulate(output_force_shape.begin(), output_force_shape.end(), 1, - std::multiplies()); - std::vector output_virial_shape = virial_->shape(); - int output_virial_size = - std::accumulate(output_virial_shape.begin(), output_virial_shape.end(), 1, - std::multiplies()); + 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()); @@ -243,20 +264,19 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, select_map(force, dforce, bkw_map, 3, nframes, fwd_map.size(), nall_real); if (atomic) { - throw deepmd::deepmd_exception( - "Atomic virial is not supported as output yet."); - // auto atom_virial_ = predictor->GetOutputHandle("extended_virial"); - // auto atom_energy_ = predictor->GetOutputHandle("atom_energy"); - // datom_energy.resize(nall_real, - // 0.0); // resize to nall to be consistenet with TF. - // atom_energy_->CopyToCpu(datom_energy.data()); - // 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); + 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>( @@ -325,17 +345,16 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, } std::unique_ptr fparam_tensor; if (!fparam.empty()) { - throw deepmd::deepmd_exception("fparam is not supported as input yet."); - // fparam_tensor = predictor->GetInputHandle("box"); - // fparam_tensor->Reshape({1, static_cast(fparam.size())}); - // fparam_tensor->CopyFromCpu((fparam.data())); + 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()) { - throw deepmd::deepmd_exception("fparam is not supported as input yet."); - // aparam_tensor = predictor->GetInputHandle("box"); - // aparam_tensor->Reshape({1, natoms, static_cast(aparam.size()) / - // natoms}); aparam_tensor->CopyFromCpu((aparam.data())); + 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; @@ -344,9 +363,9 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, } auto output_names = predictor->GetOutputNames(); - auto energy_ = predictor->GetOutputHandle(output_names[1]); - auto force_ = predictor->GetOutputHandle(output_names[2]); - auto virial_ = predictor->GetOutputHandle(output_names[4]); + 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); @@ -364,18 +383,16 @@ void DeepPotPD::compute(ENERGYVTYPE& ener, virial_->CopyToCpu(virial.data()); if (atomic) { - throw deepmd::deepmd_exception( - "atomic virial is not supported as output yet."); - // auto atom_energy_ = predictor->GetOutputHandle(output_names[0]); - // auto atom_virial_ = predictor->GetOutputHandle(output_names[5]); - // 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_virial.resize(atom_virial_numel); - // atom_energy_->CopyToCpu(atom_energy.data()); - // atom_virial_->CopyToCpu(atom_virial.data()); + 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()); } } @@ -409,9 +426,7 @@ template void DeepPotPD::compute>( that need to be postprocessed */ void DeepPotPD::get_type_map(std::string& type_map) { auto type_map_tensor = predictor->GetOutputHandle("buffer_type_map"); - auto type_map_shape = type_map_tensor->shape(); - int type_map_size = std::accumulate( - type_map_shape.begin(), type_map_shape.end(), 1, std::multiplies()); + 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()); @@ -425,9 +440,7 @@ template void DeepPotPD::get_buffer(const std::string& buffer_name, std::vector& buffer_array) { auto buffer_tensor = predictor->GetOutputHandle(buffer_name); - auto buffer_shape = buffer_tensor->shape(); - int buffer_size = std::accumulate(buffer_shape.begin(), buffer_shape.end(), 1, - std::multiplies()); + int buffer_size = numel(*buffer_tensor); buffer_array.resize(buffer_size); buffer_tensor->CopyToCpu(buffer_array.data()); } diff --git a/source/api_cc/tests/test_deeppot_pd.cc b/source/api_cc/tests/test_deeppot_pd.cc index c22257614f..f39aa84640 100644 --- a/source/api_cc/tests/test_deeppot_pd.cc +++ b/source/api_cc/tests/test_deeppot_pd.cc @@ -114,56 +114,56 @@ TYPED_TEST(TestInferDeepPotAPd, cpu_build_nlist) { } } -// 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_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; @@ -263,90 +263,88 @@ TYPED_TEST(TestInferDeepPotAPd, cpu_lmp_nlist) { } } -// 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); +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_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); -// } + 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); + 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_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); -// } -// } + 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; @@ -472,69 +470,68 @@ TYPED_TEST(TestInferDeepPotAPd, cpu_lmp_nlist_type_sel) { } } -// 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); +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(); -// EXPECT_EQ(force.size(), natoms * 3); -// EXPECT_EQ(virial.size(), 9); + // 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()); -// 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); -// } -// } + // 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; @@ -548,7 +545,8 @@ class TestInferDeepPotAPdNoPbc : public ::testing::Test { 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 = {}; + 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}; @@ -602,7 +600,11 @@ class TestInferDeepPotAPdNoPbc : public ::testing::Test { void TearDown() override { remove("deeppot.pb"); }; }; -TYPED_TEST_SUITE(TestInferDeepPotAPdNoPbc, PDValueTypes); +/* +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; diff --git a/source/tests/infer/deeppot_sea.forward_lower.json b/source/tests/infer/deeppot_sea.forward_lower.json index fabe572b81..ad99565325 100644 --- a/source/tests/infer/deeppot_sea.forward_lower.json +++ b/source/tests/infer/deeppot_sea.forward_lower.json @@ -1 +1 @@ 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