1 // SPDX-FileCopyrightText: Copyright (c) 2017-2024, HONEE contributors. 2 // SPDX-License-Identifier: Apache-2.0 OR BSD-2-Clause 3 4 #include "../../qfunctions/sgs_dd_training.h" 5 6 #include <differential_filter.h> 7 #include <navierstokes.h> 8 #include <petscdmplex.h> 9 #include <smartsim-impl.h> 10 11 typedef struct { 12 DM dm_dd_training; 13 PetscInt num_comp_dd_inputs, write_data_interval, num_filter_widths; 14 PetscScalar filter_widths[16]; 15 OperatorApplyContext op_training_data_calc_ctx; 16 DiffFilterData diff_filter; 17 NodalProjectionData filtered_grad_velo_proj; 18 size_t training_data_array_dims[2]; 19 PetscBool overwrite_training_data; 20 } *SGS_DD_TrainingData; 21 22 #define SGS_DD_TRAIN_KEY "SGS Data Driven Training" 23 24 static PetscErrorCode SGS_DD_TrainingDataDestroy(SGS_DD_TrainingData *sgs_dd_train) { 25 SGS_DD_TrainingData sgs_dd_train_ = *sgs_dd_train; 26 27 PetscFunctionBeginUser; 28 if (!sgs_dd_train_) PetscFunctionReturn(PETSC_SUCCESS); 29 PetscCall(OperatorApplyContextDestroy(sgs_dd_train_->op_training_data_calc_ctx)); 30 PetscCall(NodalProjectionDataDestroy(&sgs_dd_train_->filtered_grad_velo_proj)); 31 PetscCall(DMDestroy(&sgs_dd_train_->dm_dd_training)); 32 PetscCall(DifferentialFilterDataDestroy(&sgs_dd_train_->diff_filter)); 33 PetscCall(PetscFree(sgs_dd_train_)); 34 *sgs_dd_train = NULL; 35 PetscFunctionReturn(PETSC_SUCCESS); 36 } 37 38 typedef struct { 39 CeedElemRestriction elem_restr_grid_aniso; 40 CeedVector grid_aniso_ceed; 41 CeedQFunctionContext sgs_dd_train_qfctx; 42 } *SGS_DD_TrainingSetupData; 43 44 static PetscErrorCode SGS_DD_TrainingSetupDataDestroy(SGS_DD_TrainingSetupData sgs_dd_train_setup_data) { 45 Ceed ceed; 46 47 PetscFunctionBeginUser; 48 PetscCall(CeedElemRestrictionGetCeed(sgs_dd_train_setup_data->elem_restr_grid_aniso, &ceed)); 49 50 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&sgs_dd_train_setup_data->elem_restr_grid_aniso)); 51 PetscCallCeed(ceed, CeedVectorDestroy(&sgs_dd_train_setup_data->grid_aniso_ceed)); 52 PetscCallCeed(ceed, CeedQFunctionContextDestroy(&sgs_dd_train_setup_data->sgs_dd_train_qfctx)); 53 PetscCall(PetscFree(sgs_dd_train_setup_data)); 54 PetscCheck(CeedDestroy(&ceed) == CEED_ERROR_SUCCESS, PETSC_COMM_SELF, PETSC_ERR_LIB, "Destroying Ceed object failed"); 55 PetscFunctionReturn(PETSC_SUCCESS); 56 } 57 58 // @brief Create DM for storing data-drive SGS model inputs 59 static PetscErrorCode SGS_DD_TrainingCreateDM(DM dm_source, DM *dm_dd_training, PetscInt degree, PetscInt q_extra, PetscInt *num_components) { 60 PetscSection section; 61 62 PetscFunctionBeginUser; 63 *num_components = 12; 64 65 PetscCall(DMClone(dm_source, dm_dd_training)); 66 PetscCall(DMSetMatrixPreallocateSkip(*dm_dd_training, PETSC_TRUE)); 67 PetscCall(PetscObjectSetName((PetscObject)*dm_dd_training, "Data-Driven SGS Training Data")); 68 69 PetscCall(DMSetupByOrder_FEM(PETSC_TRUE, PETSC_TRUE, degree, 1, q_extra, 1, num_components, *dm_dd_training)); 70 71 PetscCall(DMGetLocalSection(*dm_dd_training, §ion)); 72 PetscCall(PetscSectionSetFieldName(section, 0, "Data-Driven SGS Training Data")); 73 PetscCall(PetscSectionSetComponentName(section, 0, 0, "SGSInput1")); 74 PetscCall(PetscSectionSetComponentName(section, 0, 1, "SGSInput2")); 75 PetscCall(PetscSectionSetComponentName(section, 0, 2, "SGSInput3")); 76 PetscCall(PetscSectionSetComponentName(section, 0, 3, "SGSInput4")); 77 PetscCall(PetscSectionSetComponentName(section, 0, 4, "SGSInput5")); 78 PetscCall(PetscSectionSetComponentName(section, 0, 5, "SGSInput6")); 79 PetscCall(PetscSectionSetComponentName(section, 0, 6, "FilteredSGSXX")); 80 PetscCall(PetscSectionSetComponentName(section, 0, 7, "FilteredSGSYY")); 81 PetscCall(PetscSectionSetComponentName(section, 0, 8, "FilteredSGSZZ")); 82 PetscCall(PetscSectionSetComponentName(section, 0, 9, "FilteredSGSYZ")); 83 PetscCall(PetscSectionSetComponentName(section, 0, 10, "FilteredSGSXZ")); 84 PetscCall(PetscSectionSetComponentName(section, 0, 11, "FilteredSGSXY")); 85 PetscFunctionReturn(PETSC_SUCCESS); 86 }; 87 88 // @brief Create CeedOperator to calculate training data for data-drive SGS model at nodes 89 static PetscErrorCode SetupTrainingDataCalculation(Ceed ceed, Honee honee, ProblemData problem, SGS_DD_TrainingSetupData sgs_dd_train_setup_data) { 90 SGS_DD_TrainingData sgs_dd_train; 91 CeedQFunction qf_sgs_dd_train; 92 CeedOperator op_sgs_dd_train; 93 CeedInt num_comp_grad_velo, num_comp_grid_aniso; 94 CeedVector inv_multiplicity, filtered_fields; 95 CeedElemRestriction elem_restr_inv_multiplicity, elem_restr_grad_velo, elem_restr_sgs_train; 96 DMLabel domain_label = NULL; 97 PetscInt label_value = 0, height = 0, dm_field = 0; 98 99 PetscFunctionBeginUser; 100 PetscCall(HoneeGetContainer(honee, SGS_DD_TRAIN_KEY, &sgs_dd_train)); 101 PetscCallCeed(ceed, CeedElemRestrictionGetNumComponents(sgs_dd_train_setup_data->elem_restr_grid_aniso, &num_comp_grid_aniso)); 102 103 PetscCall(DMPlexCeedElemRestrictionCreate(ceed, sgs_dd_train->dm_dd_training, domain_label, label_value, height, dm_field, &elem_restr_sgs_train)); 104 PetscCall(GetInverseMultiplicity(ceed, sgs_dd_train->dm_dd_training, domain_label, label_value, height, dm_field, PETSC_TRUE, 105 &elem_restr_inv_multiplicity, &inv_multiplicity)); 106 107 CeedElemRestriction elem_restr_filtered_state; 108 CeedInt num_comp_filtered_state; 109 { // -- Setup filtered velocity gradient projection 110 CeedBasis basis_filtered_state; 111 CeedOperatorField op_field; 112 PetscCallCeed(ceed, CeedOperatorGetFieldByName(sgs_dd_train->diff_filter->op_rhs_ctx->op, "v0", &op_field)); 113 PetscCallCeed(ceed, CeedOperatorFieldGetData(op_field, NULL, &elem_restr_filtered_state, &basis_filtered_state, NULL)); 114 PetscCallCeed(ceed, CeedElemRestrictionGetNumComponents(elem_restr_filtered_state, &num_comp_filtered_state)); 115 PetscCall(VelocityGradientProjectionSetup(ceed, honee, problem, STATEVAR_PRIMITIVE, elem_restr_filtered_state, basis_filtered_state, 116 &sgs_dd_train->filtered_grad_velo_proj)); 117 PetscCallCeed(ceed, CeedBasisDestroy(&basis_filtered_state)); 118 // Get velocity gradient information 119 PetscCallCeed(ceed, CeedOperatorGetFieldByName(sgs_dd_train->filtered_grad_velo_proj->l2_rhs_ctx->op, "velocity gradient", &op_field)); 120 PetscCallCeed(ceed, CeedOperatorFieldGetElemRestriction(op_field, &elem_restr_grad_velo)); 121 PetscCallCeed(ceed, CeedElemRestrictionGetNumComponents(elem_restr_grad_velo, &num_comp_grad_velo)); 122 } 123 124 CeedElemRestriction elem_restr_filtered_velo_prod; 125 CeedInt num_comp_filtered_velo_prod; 126 { // Get filtered velocity product information 127 CeedOperatorField op_field; 128 PetscCallCeed(ceed, CeedOperatorGetFieldByName(sgs_dd_train->diff_filter->op_rhs_ctx->op, "v1", &op_field)); 129 PetscCallCeed(ceed, CeedOperatorFieldGetElemRestriction(op_field, &elem_restr_filtered_velo_prod)); 130 PetscCallCeed(ceed, CeedElemRestrictionGetNumComponents(elem_restr_filtered_velo_prod, &num_comp_filtered_velo_prod)); 131 } 132 133 // -- Create operator for generating training data at nodes 134 // Differential Filter only provides filtered primitive variables 135 PetscCallCeed(ceed, CeedQFunctionCreateInterior(ceed, 1, ComputeSGS_DDAnisotropicTrainingDataNodal_Prim, 136 ComputeSGS_DDAnisotropicTrainingDataNodal_Prim_loc, &qf_sgs_dd_train)); 137 138 PetscCallCeed(ceed, CeedQFunctionSetContext(qf_sgs_dd_train, sgs_dd_train_setup_data->sgs_dd_train_qfctx)); 139 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_sgs_dd_train, "q", num_comp_filtered_state, CEED_EVAL_NONE)); 140 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_sgs_dd_train, "velocity product", num_comp_filtered_velo_prod, CEED_EVAL_NONE)); 141 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_sgs_dd_train, "gradient velocity", num_comp_grad_velo, CEED_EVAL_NONE)); 142 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_sgs_dd_train, "anisotropy tensor", num_comp_grid_aniso, CEED_EVAL_NONE)); 143 PetscCallCeed(ceed, CeedQFunctionAddInput(qf_sgs_dd_train, "inverse multiplicity", 1, CEED_EVAL_NONE)); 144 PetscCallCeed(ceed, CeedQFunctionAddOutput(qf_sgs_dd_train, "training data", sgs_dd_train->num_comp_dd_inputs, CEED_EVAL_NONE)); 145 146 PetscCallCeed(ceed, CeedElemRestrictionCreateVector(elem_restr_filtered_state, &filtered_fields, NULL)); 147 PetscCallCeed(ceed, CeedOperatorCreate(ceed, qf_sgs_dd_train, NULL, NULL, &op_sgs_dd_train)); 148 PetscCallCeed(ceed, CeedOperatorSetField(op_sgs_dd_train, "q", elem_restr_filtered_state, CEED_BASIS_NONE, filtered_fields)); 149 PetscCallCeed(ceed, CeedOperatorSetField(op_sgs_dd_train, "velocity product", elem_restr_filtered_velo_prod, CEED_BASIS_NONE, filtered_fields)); 150 PetscCallCeed(ceed, CeedOperatorSetField(op_sgs_dd_train, "gradient velocity", elem_restr_grad_velo, CEED_BASIS_NONE, CEED_VECTOR_ACTIVE)); 151 PetscCallCeed(ceed, CeedOperatorSetField(op_sgs_dd_train, "anisotropy tensor", sgs_dd_train_setup_data->elem_restr_grid_aniso, CEED_BASIS_NONE, 152 sgs_dd_train_setup_data->grid_aniso_ceed)); 153 PetscCallCeed(ceed, CeedOperatorSetField(op_sgs_dd_train, "inverse multiplicity", elem_restr_inv_multiplicity, CEED_BASIS_NONE, inv_multiplicity)); 154 PetscCallCeed(ceed, CeedOperatorSetField(op_sgs_dd_train, "training data", elem_restr_sgs_train, CEED_BASIS_NONE, CEED_VECTOR_ACTIVE)); 155 156 PetscCall(OperatorApplyContextCreate(sgs_dd_train->filtered_grad_velo_proj->dm, sgs_dd_train->dm_dd_training, ceed, op_sgs_dd_train, NULL, NULL, 157 NULL, NULL, &sgs_dd_train->op_training_data_calc_ctx)); 158 159 PetscCallCeed(ceed, CeedVectorDestroy(&inv_multiplicity)); 160 PetscCallCeed(ceed, CeedVectorDestroy(&filtered_fields)); 161 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&elem_restr_inv_multiplicity)); 162 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&elem_restr_filtered_state)); 163 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&elem_restr_grad_velo)); 164 PetscCallCeed(ceed, CeedElemRestrictionDestroy(&elem_restr_filtered_velo_prod)); 165 PetscCallCeed(ceed, CeedQFunctionDestroy(&qf_sgs_dd_train)); 166 PetscCallCeed(ceed, CeedOperatorDestroy(&op_sgs_dd_train)); 167 PetscFunctionReturn(PETSC_SUCCESS); 168 } 169 170 PetscErrorCode SGS_DD_TrainingSetup(Ceed ceed, Honee honee) { 171 SGS_DDTrainingContext sgsdd_train_ctx; 172 SGS_DD_TrainingSetupData sgs_dd_train_setup_data; 173 SGS_DD_TrainingData sgs_dd_train; 174 ProblemData problem = honee->problem_data; 175 176 PetscFunctionBeginUser; 177 178 PetscCall(PetscNew(&sgsdd_train_ctx)); 179 PetscCall(PetscNew(&sgs_dd_train_setup_data)); 180 PetscCall(PetscNew(&sgs_dd_train)); 181 PetscCall(HoneeSetContainer(honee, SGS_DD_TRAIN_KEY, sgs_dd_train, (PetscCtxDestroyFn *)SGS_DD_TrainingDataDestroy)); 182 PetscCall(DifferentialFilterSetup(honee, &sgs_dd_train->diff_filter)); 183 184 sgs_dd_train->overwrite_training_data = PETSC_TRUE; 185 sgs_dd_train->write_data_interval = 1; 186 sgs_dd_train->num_filter_widths = sizeof(sgs_dd_train->filter_widths) / sizeof(sgs_dd_train->filter_widths[0]); 187 PetscOptionsBegin(honee->comm, NULL, "SGS Data-Driven Training Options", NULL); 188 PetscCall(PetscOptionsInt("-sgs_train_write_data_interval", "Number of timesteps between writing data into database", NULL, 189 sgs_dd_train->write_data_interval, &sgs_dd_train->write_data_interval, NULL)); 190 PetscCall(PetscOptionsBool("-sgs_train_overwrite_data", "Overwrite old training data in the database", NULL, sgs_dd_train->overwrite_training_data, 191 &sgs_dd_train->overwrite_training_data, NULL)); 192 PetscCall(PetscOptionsRealArray("-sgs_train_filter_width_scales", "Scales of each filter width put into training database", NULL, 193 sgs_dd_train->filter_widths, &sgs_dd_train->num_filter_widths, NULL)); 194 PetscOptionsEnd(); 195 196 // -- Create DM for storing training data 197 PetscCall(SGS_DD_TrainingCreateDM(honee->dm, &sgs_dd_train->dm_dd_training, honee->app_ctx->degree, honee->app_ctx->q_extra, 198 &sgs_dd_train->num_comp_dd_inputs)); 199 200 { // -- Create QFunction Context 201 NewtonianIdealGasContext gas; 202 PetscCallCeed(ceed, CeedQFunctionContextGetDataRead(problem->apply_vol_ifunction.qfctx, CEED_MEM_HOST, &gas)); 203 sgsdd_train_ctx->gas = *gas; 204 PetscCallCeed(ceed, CeedQFunctionContextRestoreDataRead(problem->apply_vol_ifunction.qfctx, &gas)); 205 PetscCallCeed(ceed, CeedQFunctionContextCreate(honee->ceed, &sgs_dd_train_setup_data->sgs_dd_train_qfctx)); 206 PetscCallCeed(ceed, CeedQFunctionContextSetData(sgs_dd_train_setup_data->sgs_dd_train_qfctx, CEED_MEM_HOST, CEED_USE_POINTER, 207 sizeof(*sgsdd_train_ctx), sgsdd_train_ctx)); 208 PetscCallCeed(ceed, CeedQFunctionContextSetDataDestroy(sgs_dd_train_setup_data->sgs_dd_train_qfctx, CEED_MEM_HOST, FreeContextPetsc)); 209 } 210 211 { // -- Send training data array info to SmartRedis database 212 PetscMPIInt rank, num_ranks; 213 SmartSimData smartsim; 214 PetscCall(HoneeGetSmartSimData(honee, &smartsim)); 215 PetscCallMPI(MPI_Comm_rank(honee->comm, &rank)); 216 PetscCallMPI(MPI_Comm_size(honee->comm, &num_ranks)); 217 218 { 219 PetscSection global_section; 220 PetscInt num_dofs, num_comps, local_min_max[2] = {0.}, global_min_max[2] = {0.}; 221 222 PetscCall(DMGetGlobalSection(sgs_dd_train->dm_dd_training, &global_section)); 223 PetscCall(DMGetGlobalVectorInfo(sgs_dd_train->dm_dd_training, &num_dofs, NULL, NULL)); 224 PetscCall(PetscSectionGetFieldComponents(global_section, 0, &num_comps)); 225 local_min_max[0] = num_dofs; 226 PetscCall(PetscGlobalMinMaxInt(honee->comm, local_min_max, global_min_max)); 227 228 sgs_dd_train->training_data_array_dims[0] = global_min_max[0] / num_comps; 229 sgs_dd_train->training_data_array_dims[1] = num_comps; 230 } 231 232 if (rank % smartsim->collocated_database_num_ranks == 0) { 233 { // Communicate info on simulation size 234 const char tensor_name[] = "sizeInfo"; 235 size_t array_info_dim = 6; 236 PetscInt64 array_info[6] = {0}, num_features = 6; 237 238 array_info[0] = sgs_dd_train->training_data_array_dims[0]; 239 array_info[1] = sgs_dd_train->training_data_array_dims[1]; 240 array_info[2] = num_features; 241 array_info[3] = num_ranks; 242 array_info[4] = smartsim->collocated_database_num_ranks; 243 array_info[5] = rank; 244 245 PetscCall(PetscLogEventBegin(HONEE_SmartRedis_Meta, 0, 0, 0, 0)); 246 PetscCallSmartRedis( 247 put_tensor(smartsim->client, tensor_name, strlen(tensor_name), array_info, &array_info_dim, 1, SRTensorTypeInt64, SRMemLayoutContiguous)); 248 PetscCall(SmartRedisVerifyPutTensor(smartsim->client, tensor_name, strlen(tensor_name))); 249 PetscCall(PetscLogEventEnd(HONEE_SmartRedis_Meta, 0, 0, 0, 0)); 250 } 251 252 { // Send array that communicates if tensors are overwritten in database 253 const char tensor_name[] = "tensor-ow"; 254 PetscInt64 tensor_overwrite[2] = {sgs_dd_train->overwrite_training_data}; 255 size_t dim_2[1] = {2}; 256 257 PetscCall(PetscLogEventBegin(HONEE_SmartRedis_Meta, 0, 0, 0, 0)); 258 PetscCallSmartRedis( 259 put_tensor(smartsim->client, tensor_name, strlen(tensor_name), tensor_overwrite, dim_2, 1, SRTensorTypeInt64, SRMemLayoutContiguous)); 260 PetscCall(SmartRedisVerifyPutTensor(smartsim->client, tensor_name, strlen(tensor_name))); 261 PetscCall(PetscLogEventEnd(HONEE_SmartRedis_Meta, 0, 0, 0, 0)); 262 } 263 264 { // Communicate number of filter widths used 265 const char tensor_name[] = "num_filter_widths"; 266 PetscInt64 num_filter_widths = sgs_dd_train->num_filter_widths; 267 size_t dim_2 = 1; 268 269 PetscCall(PetscLogEventBegin(HONEE_SmartRedis_Meta, 0, 0, 0, 0)); 270 PetscCallSmartRedis( 271 put_tensor(smartsim->client, tensor_name, strlen(tensor_name), &num_filter_widths, &dim_2, 1, SRTensorTypeInt64, SRMemLayoutContiguous)); 272 PetscCall(SmartRedisVerifyPutTensor(smartsim->client, tensor_name, strlen(tensor_name))); 273 PetscCall(PetscLogEventEnd(HONEE_SmartRedis_Meta, 0, 0, 0, 0)); 274 } 275 } 276 } 277 278 // -- Compute and store anisotropy tensor 279 PetscCall(GridAnisotropyTensorProjectionSetupApply(ceed, honee, &sgs_dd_train_setup_data->elem_restr_grid_aniso, 280 &sgs_dd_train_setup_data->grid_aniso_ceed)); 281 282 // -- Create Nodal Evaluation Operator 283 PetscCall(SetupTrainingDataCalculation(ceed, honee, problem, sgs_dd_train_setup_data)); 284 285 PetscCall(SGS_DD_TrainingSetupDataDestroy(sgs_dd_train_setup_data)); 286 PetscFunctionReturn(PETSC_SUCCESS); 287 } 288 289 PetscErrorCode TSMonitor_SGS_DD_Training(TS ts, PetscInt step_num, PetscReal solution_time, Vec Q, void *ctx) { 290 Honee honee = (Honee)ctx; 291 Ceed ceed = honee->ceed; 292 SGS_DD_TrainingData sgs_dd_train; 293 SmartSimData smartsim; 294 Vec TrainingData; 295 PetscMPIInt rank; 296 297 PetscFunctionBeginUser; 298 PetscCall(HoneeGetSmartSimData(honee, &smartsim)); 299 PetscCall(HoneeGetContainer(honee, SGS_DD_TRAIN_KEY, &sgs_dd_train)); 300 PetscCallMPI(MPI_Comm_rank(honee->comm, &rank)); 301 302 if (step_num % sgs_dd_train->write_data_interval != 0) PetscFunctionReturn(PETSC_SUCCESS); 303 PetscCall(DMGetGlobalVector(sgs_dd_train->dm_dd_training, &TrainingData)); 304 305 for (PetscInt filter_index = 0; filter_index < sgs_dd_train->num_filter_widths; filter_index++) { 306 PetscCall(PetscLogEventBegin(HONEE_TrainDataCompute, 0, 0, 0, 0)); 307 { // -- Compute and assemble training data 308 Vec FilteredVelocityGradient, FilteredFields, FilteredFields_loc; 309 PetscMemType filtered_fields_mem_type; 310 CeedVector filtered_fields; 311 312 { // Set filter width for the current solve 313 double filter_width_scaling[3]; 314 CeedOperator op_mat; 315 Mat A_mat; 316 317 for (int j = 0; j < 3; j++) filter_width_scaling[j] = sgs_dd_train->filter_widths[filter_index]; 318 PetscCall(KSPGetOperators(sgs_dd_train->diff_filter->ksp, &A_mat, NULL)); 319 PetscCall(MatCeedGetCeedOperators(A_mat, &op_mat, NULL)); 320 PetscCall(CeedOperatorSetContextDouble(op_mat, sgs_dd_train->diff_filter->filter_width_scaling_label, filter_width_scaling)); 321 } 322 323 PetscCall(DMGetGlobalVector(sgs_dd_train->diff_filter->dm_filter, &FilteredFields)); 324 PetscCall(DMGetLocalVector(sgs_dd_train->diff_filter->dm_filter, &FilteredFields_loc)); 325 326 PetscCall(DifferentialFilterApply(honee, sgs_dd_train->diff_filter, solution_time, Q, FilteredFields)); 327 PetscCall(DMGlobalToLocal(sgs_dd_train->diff_filter->dm_filter, FilteredFields, INSERT_VALUES, FilteredFields_loc)); 328 329 PetscCall(DMGetGlobalVector(sgs_dd_train->filtered_grad_velo_proj->dm, &FilteredVelocityGradient)); 330 PetscCall(VelocityGradientProjectionApply(sgs_dd_train->filtered_grad_velo_proj, FilteredFields_loc, FilteredVelocityGradient)); 331 332 { 333 CeedOperatorField op_field; 334 335 PetscCallCeed(ceed, CeedOperatorGetFieldByName(sgs_dd_train->op_training_data_calc_ctx->op, "q", &op_field)); 336 PetscCallCeed(ceed, CeedOperatorFieldGetVector(op_field, &filtered_fields)); 337 } 338 339 PetscCall(VecPetscToCeed(FilteredFields_loc, &filtered_fields_mem_type, filtered_fields)); // filtered_fields is an implicit input 340 PetscCall(ApplyCeedOperatorGlobalToGlobal(FilteredVelocityGradient, TrainingData, sgs_dd_train->op_training_data_calc_ctx)); 341 PetscCall(VecCeedToPetsc(filtered_fields, filtered_fields_mem_type, FilteredFields_loc)); 342 343 PetscCall(DMRestoreGlobalVector(sgs_dd_train->filtered_grad_velo_proj->dm, &FilteredVelocityGradient)); 344 PetscCall(DMRestoreGlobalVector(sgs_dd_train->diff_filter->dm_filter, &FilteredFields)); 345 PetscCall(DMRestoreLocalVector(sgs_dd_train->diff_filter->dm_filter, &FilteredFields_loc)); 346 PetscCallCeed(ceed, CeedVectorDestroy(&filtered_fields)); 347 } 348 PetscCall(PetscLogEventEnd(HONEE_TrainDataCompute, 0, 0, 0, 0)); 349 350 { // -- Send training data to SmartSim 351 char array_key[PETSC_MAX_PATH_LEN]; 352 size_t array_key_len; 353 354 if (sgs_dd_train->overwrite_training_data) { 355 PetscCall(PetscSNPrintf(array_key, sizeof array_key, "%s.%" PetscInt_FMT, smartsim->rank_id_name, filter_index)); 356 } else { 357 PetscCall(PetscSNPrintf(array_key, sizeof array_key, "%s.%" PetscInt_FMT "%" PetscInt_FMT, smartsim->rank_id_name, step_num, filter_index)); 358 } 359 PetscCall(PetscStrlen(array_key, &array_key_len)); 360 361 { 362 const PetscScalar *training_data; 363 PetscCall(VecGetArrayRead(TrainingData, &training_data)); 364 PetscCall(PetscLogEventBegin(HONEE_SmartRedis_Write, 0, 0, 0, 0)); 365 PetscCallSmartRedis(put_tensor(smartsim->client, array_key, array_key_len, (void *)training_data, sgs_dd_train->training_data_array_dims, 2, 366 SRTensorTypeDouble, SRMemLayoutContiguous)); 367 PetscCall(PetscLogEventEnd(HONEE_SmartRedis_Write, 0, 0, 0, 0)); 368 PetscCall(VecRestoreArrayRead(TrainingData, &training_data)); 369 } 370 } 371 } 372 373 if (rank % smartsim->collocated_database_num_ranks == 0) { 374 const char tensor_name[] = "step"; 375 size_t dim_2[1] = {2}; 376 PetscInt64 step_array[2] = {step_num, step_num}; 377 378 PetscCall(PetscLogEventBegin(HONEE_SmartRedis_Meta, 0, 0, 0, 0)); 379 PetscCallSmartRedis( 380 put_tensor(smartsim->client, tensor_name, strlen(tensor_name), step_array, dim_2, 1, SRTensorTypeInt64, SRMemLayoutContiguous)); 381 PetscCall(PetscLogEventEnd(HONEE_SmartRedis_Meta, 0, 0, 0, 0)); 382 } 383 384 PetscCall(DMRestoreGlobalVector(sgs_dd_train->dm_dd_training, &TrainingData)); 385 PetscFunctionReturn(PETSC_SUCCESS); 386 } 387 388 PetscErrorCode TSPostStep_SGS_DD_Training(TS ts) { 389 Honee honee; 390 const char check_run_key[] = "check-run"; 391 PetscReal check_run[2] = {1}; 392 const size_t check_run_dims[1] = {2}; 393 size_t check_run_key_size; 394 SmartSimData smartsim; 395 396 PetscFunctionBeginUser; 397 PetscCall(PetscStrlen(check_run_key, &check_run_key_size)); 398 PetscCall(TSGetApplicationContext(ts, &honee)); 399 PetscCall(HoneeGetSmartSimData(honee, &smartsim)); 400 401 PetscCall(PetscLogEventBegin(HONEE_SmartRedis_Meta, 0, 0, 0, 0)); 402 PetscCallSmartRedis( 403 unpack_tensor(smartsim->client, check_run_key, check_run_key_size, check_run, check_run_dims, 1, SRTensorTypeDouble, SRMemLayoutContiguous)); 404 PetscCall(PetscLogEventEnd(HONEE_SmartRedis_Meta, 0, 0, 0, 0)); 405 if (check_run[0] == 0) { 406 PetscCall(PetscPrintf(honee->comm, "-- Simulation stopped by 'check-run' tensor in Redis database\n")); 407 PetscCall(TSSetConvergedReason(ts, TS_CONVERGED_USER)); 408 } 409 410 PetscFunctionReturn(PETSC_SUCCESS); 411 } 412