(git:71c3ab0)
Loading...
Searching...
No Matches
pao_types.F
Go to the documentation of this file.
1!--------------------------------------------------------------------------------------------------!
2! CP2K: A general program to perform molecular dynamics simulations !
3! Copyright 2000-2026 CP2K developers group <https://cp2k.org> !
4! !
5! SPDX-License-Identifier: GPL-2.0-or-later !
6!--------------------------------------------------------------------------------------------------!
7
8! **************************************************************************************************
9!> \brief Types used by the PAO machinery
10!> \author Ole Schuett
11! **************************************************************************************************
12MODULE pao_types
13 USE omp_lib, ONLY: omp_destroy_lock,&
14 omp_lock_kind
15 USE cp_dbcsr_api, ONLY: dbcsr_distribution_release,&
16 dbcsr_distribution_type,&
17 dbcsr_release,&
18 dbcsr_type
19 USE kinds, ONLY: default_path_length,&
20 default_string_length,&
21 dp
22 USE linesearch, ONLY: linesearch_type
23 USE torch_api, ONLY: torch_model_release,&
24 torch_model_type
25#include "./base/base_uses.f90"
26
27 IMPLICIT NONE
28
29 PRIVATE
30
31 CHARACTER(len=*), PARAMETER, PRIVATE :: moduleN = 'pao_types'
32
33 PUBLIC :: pao_env_type, training_matrix_type, pao_model_type, pao_finalize
34
35 TYPE filename_type
36 CHARACTER(LEN=default_path_length) :: fn = ""
37 END TYPE filename_type
38
39! **************************************************************************************************
40!> \brief PAO machine learning data for one atomic kind
41!> \var kindname name of atomic kind
42!> \var inputs training points
43!> \var outputs training points
44!> \var prior constant prior which is added to prediction
45!> \var NN trained neural network
46!> \var GP trained gaussian process
47! **************************************************************************************************
48 TYPE training_matrix_type
49 CHARACTER(LEN=default_string_length) :: kindname = ""
50 REAL(dp), DIMENSION(:, :), ALLOCATABLE :: inputs
51 REAL(dp), DIMENSION(:, :), ALLOCATABLE :: outputs
52 REAL(dp), DIMENSION(:), ALLOCATABLE :: prior
53 REAL(dp), DIMENSION(:, :, :), ALLOCATABLE :: nn ! Neural Network
54 REAL(dp), DIMENSION(:, :), ALLOCATABLE :: gp ! Gaussian Process
55 END TYPE training_matrix_type
56
57! **************************************************************************************************
58!> \brief PAO-ML model for a single atomic kind.
59! **************************************************************************************************
60 TYPE pao_model_type
61 TYPE(torch_model_type) :: torch_model
62 INTEGER :: version = -1
63 CHARACTER(LEN=default_string_length) :: kind_name = ""
64 INTEGER :: atomic_number = -1
65 CHARACTER(LEN=default_string_length) :: prim_basis_name = ""
66 INTEGER :: prim_basis_size = -1
67 INTEGER :: pao_basis_size = -1
68 INTEGER :: num_layers = -1
69 REAL(dp) :: cutoff = 0.0_dp
70 INTEGER, DIMENSION(:), ALLOCATABLE :: kinds_mapping
71 INTEGER(kind=omp_lock_kind) :: lock = -1
72 END TYPE pao_model_type
73
74! **************************************************************************************************
75!> \brief The PAO environment type
76!> \var eps_pao parsed input parameter
77!> \var cg_reset_limit parsed input parameter
78!> \var mixing parsed input parameter
79!> \var regularization parsed input parameter
80!> \var penalty_dist parsed input parameter
81!> \var penalty_strength parsed input parameter
82!> \var check_unitary_tol parsed input parameter
83!> \var check_grad_tol parsed input parameter
84!> \var num_grad_eps parsed input parameter
85!> \var eps_pgf parsed input parameter
86!> \var linpot_precon_delta parsed input parameter
87!> \var linpot_init_delta parsed input parameter
88!> \var linpot_regu_delta parsed input parameter
89!> \var linpot_regu_strength parsed input parameter
90!> \var num_grad_order parsed input parameter
91!> \var max_pao parsed input parameter
92!> \var max_cycles parsed input parameter
93!> \var write_cycles parsed input parameter
94!> \var parameterization parsed input parameter
95!> \var optimizer parsed input parameter
96!> \var cg_init_steps parsed input parameter
97!> \var preopt_dm_file parsed input parameter
98!> \var restart_file parsed input parameter
99!> \var ml_training_set parsed input parameter
100!> \var ml_method parsed input parameter
101!> \var ml_prior parsed input parameter
102!> \var ml_descriptor parsed input parameter
103!> \var ml_tolerance parsed input parameter
104!> \var gp_noise_var parsed input parameter
105!> \var gp_scale parsed input parameter
106!> \var precondition parsed input parameter
107!> \var iw output unit for pao in general
108!> \var iw_atoms output unit for one line summary for each atom
109!> \var iw_gap output unit for gap of the fock matrix
110!> \var iw_fockev output unit for eigenvalues of the fock matrix
111!> \var iw_opt output unit for pao optimizer
112!> \var iw_mlvar output unit for variances of machine learning predictions
113!> \var iw_mldata output unit for dumping training data used for machine learning
114!> \var istep counts pao iterations, ie. number of pao energy evaluations
115!> \var energy_prev energy of previous pao step
116!> \var step_start_time timestamp of when current pao step started
117!> \var norm_G frobenius-norm of matrix_G or matrix_G_preconed
118!> \var linesearch holds linesearch state
119!> \var matrix_X_ready set when matrix_X is initialized
120!> \var matrix_P_ready set when density matrix is initialized
121!> \var constants_ready set when stuff, which does not depend of atomic positions is ready
122!> \var need_initial_scf set when the initial density matrix is not self-consistend
123!> \var matrix_X parameters of pao basis, which eventually determine matrix_U. Uses diag_distribution.
124!> \var matrix_U0 constant pre-rotation which serves as initial guess for exp-parametrization.
125!> Uses diag_distribution.
126!> \var matrix_H0 Diagonal blocks of core hamiltonian, uses diag_distribution
127!> \var matrix_Y selector matrix which translates between primary and pao basis.
128!> basically a block diagonal "rectangular identity matrix". Uses s_matrix-distribution.
129!> \var matrix_N diagonal matrix filled with 1/sqrt(S) from primary overlap matrix. Uses s_matrix-distribution.
130!> \var matrix_N_diag copy of matrix_N using diag_distribution
131!> \var matrix_N_inv diagonal matrix filled with sqrt(S) from primary overlap matrix. Uses s_matrix-distribution.
132!> \var matrix_N_inv_diag copy of matrix_N_inv using diag_distribution
133!> \var matrix_X_orig copy made of matrix_X at beginning of optimization cylce, used for mixing. Uses diag_distribution.
134!> \var matrix_G derivative of pao-energy wrt to matrix_X, ie. the pao-gradient. Uses diag_distribution.
135!> \var matrix_G_prev copy of gradient from previous step, used for conjugate gradient method. Uses diag_distribution.
136!> \var matrix_D Current line-search direction, used for conjugate gradient method. Uses diag_distribution.
137!> \var matrix_D_preconed Current line-search direction with preconditioner applied.
138!> This copy is keept, because application of inverse preconditioner
139!> introduces too much numeric noise. Uses diag_distribution.
140!> \var matrix_V_terms Potential terms, used by linpot and gth parametrization, Uses diag_distribution.
141!> \var matrix_BFGS Approximate inverse hessian, used by BFGS optimizer, Uses diag_distribution.
142!> \var matrix_precon preconditioner, uses diag_distribution.
143!> \var matrix_precon_inv inverse of matrix_precon, uses diag_distribution.
144!> \var matrix_R Rgularization, uses diag_distribution
145!> \var ml_training_matrices holds training data and trained machine learning model
146!> \var diag_distribution DBCSR distribution to spreads diagonal blocks evenly across ranks
147! **************************************************************************************************
148 TYPE pao_env_type
149 ! input values
150 REAL(kind=dp) :: eps_pao = 0.0_dp
151 REAL(kind=dp) :: cg_reset_limit = 0.1_dp
152 REAL(kind=dp) :: mixing = 0.0_dp
153 REAL(kind=dp) :: regularization = 0.0_dp
154 REAL(kind=dp) :: penalty_dist = 0.0_dp
155 REAL(kind=dp) :: penalty_strength = 0.0_dp
156 REAL(kind=dp) :: check_unitary_tol = 0.0_dp
157 REAL(kind=dp) :: check_grad_tol = 0.0_dp
158 REAL(kind=dp) :: num_grad_eps = 0.0_dp
159 REAL(kind=dp) :: eps_pgf = 0.0_dp
160 REAL(kind=dp) :: linpot_precon_delta = 0.0_dp
161 REAL(kind=dp) :: linpot_init_delta = 0.0_dp
162 REAL(kind=dp) :: linpot_regu_delta = 0.0_dp
163 REAL(kind=dp) :: linpot_regu_strength = 0.0_dp
164 INTEGER :: num_grad_order = -1
165 INTEGER :: max_pao = -1
166 INTEGER :: max_cycles = -1
167 INTEGER :: write_cycles = -1
168 INTEGER :: parameterization = -1
169 INTEGER :: optimizer = -1
170 INTEGER :: cg_init_steps = -1
171 LOGICAL :: precondition = .false.
172 LOGICAL :: ignore_convergence_failure = .false.
173 CHARACTER(LEN=default_path_length) :: preopt_dm_file = ""
174 CHARACTER(LEN=default_path_length) :: restart_file = ""
175 TYPE(filename_type), DIMENSION(:), ALLOCATABLE :: ml_training_set
176
177 INTEGER :: ml_method = -1
178 INTEGER :: ml_prior = -1
179 INTEGER :: ml_descriptor = -1
180 REAL(kind=dp) :: ml_tolerance = 0.0_dp
181 REAL(kind=dp) :: gp_noise_var = 0.0_dp
182 REAL(kind=dp) :: gp_scale = 0.0_dp
183
184 ! output units
185 INTEGER :: iw = -1
186 INTEGER :: iw_atoms = -1
187 INTEGER :: iw_gap = -1
188 INTEGER :: iw_fockev = -1
189 INTEGER :: iw_opt = -1
190 INTEGER :: iw_mlvar = -1
191 INTEGER :: iw_mldata = -1
192
193 ! state variable
194 INTEGER :: istep = -1
195 REAL(kind=dp) :: energy_prev = 0.0_dp
196 REAL(kind=dp) :: step_start_time = 0.0_dp
197 REAL(kind=dp) :: norm_g = 0.0_dp
198 TYPE(linesearch_type) :: linesearch = linesearch_type()
199 LOGICAL :: matrix_x_ready = .false.
200 LOGICAL :: matrix_p_ready = .false.
201 LOGICAL :: constants_ready = .false.
202 LOGICAL :: need_initial_scf = .false.
203
204 ! matrices
205 TYPE(dbcsr_type) :: matrix_x
206 TYPE(dbcsr_type) :: matrix_u0
207 TYPE(dbcsr_type) :: matrix_h0
208 TYPE(dbcsr_type) :: matrix_y
209 TYPE(dbcsr_type) :: matrix_n
210 TYPE(dbcsr_type) :: matrix_n_diag
211 TYPE(dbcsr_type) :: matrix_n_inv
212 TYPE(dbcsr_type) :: matrix_n_inv_diag
213 TYPE(dbcsr_type) :: matrix_x_orig
214 TYPE(dbcsr_type) :: matrix_g
215 TYPE(dbcsr_type) :: matrix_g_prev
216 TYPE(dbcsr_type) :: matrix_d
217 TYPE(dbcsr_type) :: matrix_d_preconed
218 TYPE(dbcsr_type) :: matrix_v_terms
219 TYPE(dbcsr_type) :: matrix_bfgs
220 TYPE(dbcsr_type) :: matrix_precon
221 TYPE(dbcsr_type) :: matrix_precon_inv
222 TYPE(dbcsr_type) :: matrix_r
223
224 TYPE(training_matrix_type), ALLOCATABLE, &
225 DIMENSION(:) :: ml_training_matrices
226
227 TYPE(pao_model_type), ALLOCATABLE, &
228 DIMENSION(:) :: models
229
230 TYPE(dbcsr_distribution_type) :: diag_distribution
231 END TYPE pao_env_type
232
233CONTAINS
234
235! **************************************************************************************************
236!> \brief Finalize the PAO environment
237!> \param pao ...
238! **************************************************************************************************
239 SUBROUTINE pao_finalize(pao)
240 TYPE(pao_env_type) :: pao
241
242 CHARACTER(len=*), PARAMETER :: routinen = 'pao_finalize'
243
244 INTEGER :: handle, i
245
246 CALL timeset(routinen, handle)
247
248 CALL dbcsr_release(pao%matrix_X)
249 CALL dbcsr_release(pao%matrix_Y)
250 CALL dbcsr_release(pao%matrix_N)
251 CALL dbcsr_release(pao%matrix_N_diag)
252 CALL dbcsr_release(pao%matrix_N_inv)
253 CALL dbcsr_release(pao%matrix_N_inv_diag)
254 CALL dbcsr_release(pao%matrix_H0)
255
256 DEALLOCATE (pao%ml_training_set)
257 IF (ALLOCATED(pao%ml_training_matrices)) THEN
258 DEALLOCATE (pao%ml_training_matrices)
259 END IF
260
261 IF (ALLOCATED(pao%models)) THEN
262 DO i = 1, SIZE(pao%models)
263 IF (pao%models(i)%version > 0) THEN
264 CALL torch_model_release(pao%models(i)%torch_model)
265 END IF
266 CALL omp_destroy_lock(pao%models(i)%lock)
267 END DO
268 DEALLOCATE (pao%models)
269 END IF
270
271 CALL dbcsr_distribution_release(pao%diag_distribution)
272
273 !TODO: should finish printkey
274
275 CALL timestop(handle)
276
277 END SUBROUTINE pao_finalize
278
279END MODULE pao_types
cp_dbcsr_api
Definition cp_dbcsr_api.F:8
cp_dbcsr_api::dbcsr_distribution_release
subroutine, public dbcsr_distribution_release(dist)
...
Definition cp_dbcsr_api.F:643
cp_dbcsr_api::dbcsr_release
subroutine, public dbcsr_release(matrix)
...
Definition cp_dbcsr_api.F:1142
kinds
Defines the basic variable types.
Definition kinds.F:23
kinds::dp
integer, parameter, public dp
Definition kinds.F:34
kinds::default_string_length
integer, parameter, public default_string_length
Definition kinds.F:57
kinds::default_path_length
integer, parameter, public default_path_length
Definition kinds.F:58
linesearch
A generic framework to calculate step lengths for 1D line search.
Definition linesearch.F:12
pao_types
Types used by the PAO machinery.
Definition pao_types.F:12
pao_types::pao_finalize
subroutine, public pao_finalize(pao)
Finalize the PAO environment.
Definition pao_types.F:240
torch_api
Definition torch_api.F:7
torch_api::torch_model_release
subroutine, public torch_model_release(model)
Releases a Torch model and all its ressources.
Definition torch_api.F:1934
cp_dbcsr_api::dbcsr_distribution_type
Definition cp_dbcsr_api.F:182
cp_dbcsr_api::dbcsr_type
Definition cp_dbcsr_api.F:176
linesearch::linesearch_type
Definition linesearch.F:94
pao_types::pao_env_type
Definition pao_types.F:148
pao_types::pao_model_type
PAO-ML model for a single atomic kind.
Definition pao_types.F:60
pao_types::training_matrix_type
Definition pao_types.F:48
torch_api::torch_model_type
Definition torch_api.F:39