1 #include <petsc/private/pcbjkokkosimpl.h>
2
3 #if defined(PETSC_HAVE_KOKKOS_KERNELS_BATCH)
4 #include <fstream>
5
6 #include "Kokkos_Timer.hpp"
7 #include "Kokkos_Random.hpp"
8 #include "Kokkos_UnorderedMap.hpp"
9 #include "Kokkos_Sort.hpp"
10
11 /// KokkosKernels headers
12 #include "KokkosBatched_Util.hpp"
13 #include "KokkosBatched_Vector.hpp"
14
15 #include <Kokkos_ArithTraits.hpp>
16 #include <KokkosBatched_Util.hpp>
17 #include <KokkosBatched_Vector.hpp>
18 #include <KokkosBatched_Copy_Decl.hpp>
19 #include <KokkosBatched_Copy_Impl.hpp>
20 #include <KokkosBatched_AddRadial_Decl.hpp>
21 #include <KokkosBatched_AddRadial_Impl.hpp>
22 #include <KokkosBatched_Gemm_Decl.hpp>
23 #include <KokkosBatched_Gemm_Serial_Impl.hpp>
24 #include <KokkosBatched_Gemm_Team_Impl.hpp>
25 #include <KokkosBatched_Gemv_Decl.hpp>
26 // #include <KokkosBatched_Gemv_Serial_Impl.hpp>
27 #include <KokkosBatched_Gemv_Team_Impl.hpp>
28 #include <KokkosBatched_Trsm_Decl.hpp>
29 #include <KokkosBatched_Trsm_Serial_Impl.hpp>
30 #include <KokkosBatched_Trsm_Team_Impl.hpp>
31 #include <KokkosBatched_Trsv_Decl.hpp>
32 #include <KokkosBatched_Trsv_Serial_Impl.hpp>
33 #include <KokkosBatched_Trsv_Team_Impl.hpp>
34 #include <KokkosBatched_LU_Decl.hpp>
35 #include <KokkosBatched_LU_Serial_Impl.hpp>
36 #include <KokkosBatched_LU_Team_Impl.hpp>
37 #include <KokkosSparse_CrsMatrix.hpp>
38 #include "KokkosBatched_Spmv.hpp"
39 #include "KokkosBatched_CrsMatrix.hpp"
40 #include "KokkosBatched_Krylov_Handle.hpp"
41
42 #include "KokkosBatched_GMRES.hpp"
43 #include "KokkosBatched_JacobiPrec.hpp"
44
45 template <typename DeviceType, typename ValuesViewType, typename IntView, typename VectorViewType, typename KrylovHandleType>
46 struct Functor_TestBatchedTeamVectorGMRES {
47 const ValuesViewType _D;
48 const ValuesViewType _diag;
49 const IntView _r;
50 const IntView _c;
51 const VectorViewType _X;
52 const VectorViewType _B;
53 const int _N_team, _team_size, _vector_length;
54 const int _N_iteration;
55 const double _tol;
56 const int _ortho_strategy;
57 const int _scratch_pad_level;
58 KrylovHandleType _handle;
59
60 KOKKOS_INLINE_FUNCTION
Functor_TestBatchedTeamVectorGMRESFunctor_TestBatchedTeamVectorGMRES61 Functor_TestBatchedTeamVectorGMRES(const ValuesViewType &D, const IntView &r, const IntView &c, const VectorViewType &X, const VectorViewType &B, const int N_team, const int team_size, const int vector_length, const int N_iteration, const double tol, const int ortho_strategy, const int scratch_pad_level, KrylovHandleType &handle) :
62 _D(D), _r(r), _c(c), _X(X), _B(B), _N_team(N_team), _team_size(team_size), _vector_length(vector_length), _N_iteration(N_iteration), _tol(tol), _ortho_strategy(ortho_strategy), _scratch_pad_level(scratch_pad_level), _handle(handle)
63 {
64 }
65
66 KOKKOS_INLINE_FUNCTION
Functor_TestBatchedTeamVectorGMRESFunctor_TestBatchedTeamVectorGMRES67 Functor_TestBatchedTeamVectorGMRES(const ValuesViewType &D, const ValuesViewType &diag, const IntView &r, const IntView &c, const VectorViewType &X, const VectorViewType &B, const int N_team, const int team_size, const int vector_length, const int N_iteration, const double tol, int ortho_strategy, const int scratch_pad_level, KrylovHandleType &handle) :
68 _D(D), _diag(diag), _r(r), _c(c), _X(X), _B(B), _N_team(N_team), _team_size(team_size), _vector_length(vector_length), _N_iteration(N_iteration), _tol(tol), _ortho_strategy(ortho_strategy), _scratch_pad_level(scratch_pad_level), _handle(handle)
69 {
70 }
71
72 template <typename MemberType>
operator ()Functor_TestBatchedTeamVectorGMRES73 KOKKOS_INLINE_FUNCTION void operator()(const MemberType &member) const
74 {
75 const int first_matrix = static_cast<int>(member.league_rank()) * _N_team;
76 const int N = _D.extent(0);
77 const int last_matrix = (static_cast<int>(member.league_rank() + 1) * _N_team < N ? static_cast<int>(member.league_rank() + 1) * _N_team : N);
78 const int graphID = static_cast<int>(member.league_rank());
79 using TeamVectorCopy1D = KokkosBatched::TeamVectorCopy<MemberType, KokkosBatched::Trans::NoTranspose, 1>;
80
81 auto d = Kokkos::subview(_D, Kokkos::make_pair(first_matrix, last_matrix), Kokkos::ALL);
82 auto x = Kokkos::subview(_X, Kokkos::make_pair(first_matrix, last_matrix), Kokkos::ALL);
83 auto b = Kokkos::subview(_B, Kokkos::make_pair(first_matrix, last_matrix), Kokkos::ALL);
84 using ScratchPadIntViewType = Kokkos::View<typename IntView::non_const_value_type *, typename IntView::array_layout, typename IntView::execution_space::scratch_memory_space>;
85 using ScratchPadValuesViewType = Kokkos::View<typename ValuesViewType::non_const_value_type **, typename ValuesViewType::array_layout, typename ValuesViewType::execution_space::scratch_memory_space>;
86
87 using Operator = KokkosBatched::CrsMatrix<ValuesViewType, ScratchPadIntViewType>;
88 ScratchPadIntViewType r(member.team_scratch(1), _r.extent(1));
89 ScratchPadIntViewType c(member.team_scratch(1), _c.extent(1));
90
91 TeamVectorCopy1D::invoke(member, Kokkos::subview(_r, graphID, Kokkos::ALL), r);
92 TeamVectorCopy1D::invoke(member, Kokkos::subview(_c, graphID, Kokkos::ALL), c);
93 Operator A(d, r, c);
94
95 ScratchPadValuesViewType diag(member.team_scratch(1), last_matrix - first_matrix, _diag.extent(1));
96 using PrecOperator = KokkosBatched::JacobiPrec<ScratchPadValuesViewType>;
97
98 KokkosBatched::TeamVectorCopy<MemberType>::invoke(member, Kokkos::subview(_diag, Kokkos::make_pair(first_matrix, last_matrix), Kokkos::ALL), diag);
99 PrecOperator P(diag);
100 P.setComputedInverse();
101
102 KokkosBatched::TeamVectorGMRES<MemberType>::template invoke<Operator, VectorViewType, PrecOperator, KrylovHandleType>(member, A, b, x, P, _handle);
103 }
runFunctor_TestBatchedTeamVectorGMRES104 inline double run(PC pc)
105 {
106 //typedef typename ValuesViewType::value_type value_type;
107 std::string name("KokkosBatched::Test::TeamVectorGMRES");
108 Kokkos::Timer timer;
109 Kokkos::Profiling::pushRegion(name.c_str());
110
111 Kokkos::TeamPolicy<DeviceType> auto_policy(ceil(1. * _D.extent(0) / _N_team), Kokkos::AUTO(), Kokkos::AUTO());
112 Kokkos::TeamPolicy<DeviceType> tuned_policy(ceil(1. * _D.extent(0) / _N_team), _team_size, _vector_length);
113 Kokkos::TeamPolicy<DeviceType> policy;
114
115 if (_team_size < 1) policy = auto_policy;
116 else policy = tuned_policy;
117
118 _handle.set_max_iteration(_N_iteration);
119 _handle.set_tolerance(_tol);
120 _handle.set_ortho_strategy(_ortho_strategy);
121 _handle.set_scratch_pad_level(_scratch_pad_level);
122 _handle.set_compute_last_residual(true);
123
124 int maximum_iteration = _handle.get_max_iteration();
125
126 using ScalarType = typename ValuesViewType::non_const_value_type;
127 using Layout = typename ValuesViewType::array_layout;
128 using EXSP = typename ValuesViewType::execution_space;
129
130 using ViewType2D = Kokkos::View<ScalarType **, Layout, EXSP>;
131 using IntViewType1D = Kokkos::View<PetscInt *, Layout, EXSP>;
132
133 size_t bytes_1D = ViewType2D::shmem_size(_N_team, 1);
134 size_t bytes_row_ptr = IntViewType1D::shmem_size(_r.extent(1));
135 size_t bytes_col_idc = IntViewType1D::shmem_size(_c.extent(1));
136 size_t bytes_2D_1 = ViewType2D::shmem_size(_N_team, _X.extent(1));
137 size_t bytes_2D_2 = ViewType2D::shmem_size(_N_team, maximum_iteration + 1);
138
139 size_t bytes_diag = bytes_2D_1;
140 size_t bytes_tmp = 2 * bytes_2D_1 + 2 * bytes_1D + bytes_2D_2;
141
142 policy.set_scratch_size(0, Kokkos::PerTeam(bytes_tmp));
143 policy.set_scratch_size(1, Kokkos::PerTeam(bytes_col_idc + bytes_row_ptr + bytes_diag));
144 PetscCall(PetscInfo(pc, "%d scratch memory(0) = %d + %d + %d bytes_diag=%d; %d scratch memory(1); %d maximum_iterations\n", (int)bytes_tmp, 2 * (int)bytes_2D_1, 2 * (int)bytes_1D, (int)bytes_2D_2, (int)bytes_diag, (int)(bytes_row_ptr + bytes_col_idc + bytes_diag), (int)maximum_iteration));
145 exec_space().fence();
146 timer.reset();
147 Kokkos::parallel_for(name.c_str(), policy, *this);
148 exec_space().fence();
149 double sec = timer.seconds();
150
151 return sec;
152 }
153 };
154
PCApply_BJKOKKOSKERNELS(PC pc,const PetscScalar * glb_bdata,PetscScalar * glb_xdata,const PetscInt * glb_Aai,const PetscInt * glb_Aaj,const PetscScalar * glb_Aaa,const PetscInt team_size,MatInfo info,const PetscInt batch_sz,PCFailedReason * pcreason)155 PETSC_INTERN PetscErrorCode PCApply_BJKOKKOSKERNELS(PC pc, const PetscScalar *glb_bdata, PetscScalar *glb_xdata, const PetscInt *glb_Aai, const PetscInt *glb_Aaj, const PetscScalar *glb_Aaa, const PetscInt team_size, MatInfo info, const PetscInt batch_sz, PCFailedReason *pcreason)
156 {
157 PC_PCBJKOKKOS *jac = (PC_PCBJKOKKOS *)pc->data;
158 Mat A = pc->pmat;
159 const PetscInt maxit = jac->ksp->max_it, nBlk = jac->nBlocks;
160 const int Nsolves = nBlk;
161 int Nsolves_team = jac->nsolves_team, fill_idx = 0;
162 int Nloc = jac->const_block_size; // same grids
163 const int nnz = (int)info.nz_used / Nsolves; // fix for variable grid size
164 PetscReal rtol = jac->ksp->rtol;
165 const PetscScalar *glb_idiag = jac->d_idiag_k->data();
166 const PetscInt *d_bid_eqOffset = jac->d_bid_eqOffset_k->data();
167 const PetscInt *d_isicol = jac->d_isicol_k->data(), *d_isrow = jac->d_isrow_k->data();
168
169 PetscFunctionBegin;
170 if (Nsolves_team > batch_sz) Nsolves_team = batch_sz; // silently fix this
171 PetscCheck(jac->const_block_size, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONG, "Kokkos (GMRES) solver requires constant block size (but can be made to work with species ordering or N_team==1)");
172 PetscCheck(Nsolves % Nsolves_team == 0, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONG, "Nsolves.mod(Nsolves_team) != 0: Nsolves = %d, Nsolves_team = %d", Nsolves, Nsolves_team);
173 PetscCheck(((int)info.nz_used) % Nsolves == 0, PetscObjectComm((PetscObject)pc), PETSC_ERR_ARG_WRONG, "info.nz_used.mod(Nsolves) != 0: info.nz_used = %g, Nsolves = %d", info.nz_used, Nsolves);
174 #if defined(PETSC_HAVE_CUDA)
175 nvtxRangePushA("gmres-kk");
176 #endif
177 Kokkos::View<PetscScalar **, layout, exec_space, Kokkos::MemoryTraits<Kokkos::Unmanaged>> inv_diag((PetscScalar *)glb_idiag, Nsolves, Nloc); // in correct order
178 if (!jac->rowOffsets) {
179 jac->rowOffsets = new IntView("rowOffsets", Nsolves / Nsolves_team, Nloc + 1); // same grids
180 jac->colIndices = new IntView("colIndices", Nsolves / Nsolves_team, nnz);
181 jac->batch_b = new XYType("batch rhs", Nsolves, Nloc);
182 jac->batch_x = new XYType("batch sol", Nsolves, Nloc);
183 jac->batch_values = new AMatrixValueView("batch values", Nsolves, nnz);
184 fill_idx = 1;
185 PetscCall(PetscInfo(pc, "Setup indices Nloc=%d, nnz=%d\n", Nloc, nnz));
186 }
187 IntView &rowOffsets = *jac->rowOffsets;
188 IntView &colIndices = *jac->colIndices;
189 XYType &batch_b = *jac->batch_b;
190 XYType &batch_x = *jac->batch_x;
191 AMatrixValueView &batch_values = *jac->batch_values;
192
193 Kokkos::deep_copy(batch_x, 0.);
194 PetscCall(PetscInfo(pc, "\tjac->n = %d, Nloc = %d, Nsolves = %d, nnz = %d, Nsolves_team = %d, league size = %d, maxit = %d\n", (int)jac->n, Nloc, Nsolves, nnz, Nsolves_team, Nsolves / Nsolves_team, (int)maxit));
195 Kokkos::parallel_for(
196 "rowOffsets+map", Kokkos::TeamPolicy<>(Nsolves, team_size, PCBJKOKKOS_VEC_SIZE), KOKKOS_LAMBDA(const team_member team) {
197 const int blkID = team.league_rank(), start = d_bid_eqOffset[blkID], end = d_bid_eqOffset[blkID + 1];
198 if (fill_idx) {
199 if (blkID % Nsolves_team == 0) { // first matrix on this member
200 Kokkos::parallel_for(Kokkos::TeamVectorRange(team, start, end), [=](const int rowb) { // Nloc
201 int rowa = d_isicol[rowb];
202 int n = glb_Aai[rowa + 1] - glb_Aai[rowa];
203 rowOffsets(blkID / Nsolves_team, rowb + 1 - start) = n; // save sizes
204 });
205 }
206 }
207 // map b into field major space
208 Kokkos::parallel_for(Kokkos::TeamVectorRange(team, start, end), [=](int rowb) {
209 int rowa = d_isicol[rowb];
210 batch_b(blkID, rowb - start) = glb_bdata[rowa];
211 });
212 });
213 Kokkos::fence();
214 if (fill_idx) {
215 Kokkos::parallel_for(
216 "prefix sum", Kokkos::TeamPolicy<>(Nsolves / Nsolves_team, 1, 1), KOKKOS_LAMBDA(const team_member team) {
217 const int graphID = team.league_rank();
218 rowOffsets(graphID, 0) = 0;
219 for (int i = 0; i < Nloc; ++i) rowOffsets(graphID, i + 1) += rowOffsets(graphID, i);
220 });
221 Kokkos::fence();
222 }
223 Kokkos::parallel_for(
224 "copy matrix", Kokkos::TeamPolicy<>(Nsolves /* /batch_sz */, team_size, PCBJKOKKOS_VEC_SIZE), KOKKOS_LAMBDA(const team_member team) {
225 const int blkID = team.league_rank(), start = d_bid_eqOffset[blkID], end = d_bid_eqOffset[blkID + 1], graphID = blkID / Nsolves_team;
226 Kokkos::parallel_for(Kokkos::TeamThreadRange(team, start, end), [=](const int rowb) {
227 int rowa = d_isicol[rowb];
228 int n = glb_Aai[rowa + 1] - glb_Aai[rowa];
229 const PetscInt *aj = glb_Aaj + glb_Aai[rowa]; // global index
230 const PetscScalar *aa = glb_Aaa + glb_Aai[rowa];
231 Kokkos::parallel_for(Kokkos::ThreadVectorRange(team, n), [=](const int &i) {
232 PetscScalar val = aa[i];
233 if (fill_idx && blkID % Nsolves_team == 0) colIndices(graphID, rowOffsets(graphID, rowb - start) + i) = d_isrow[aj[i]] - blkID * Nloc; // local" global - block start
234 batch_values(blkID, rowOffsets(graphID, rowb - start) + i) = val;
235 });
236 });
237 });
238 Kokkos::fence();
239 // setup solver
240 using ScalarType = typename AMatrixValueView::non_const_value_type;
241 using MagnitudeType = typename Kokkos::Details::ArithTraits<ScalarType>::mag_type;
242 //using NormViewType = Kokkos::View<MagnitudeType *, layout, exec_space>;
243 using Norm2DViewType = Kokkos::View<MagnitudeType **, layout, exec_space>;
244 using Scalar3DViewType = Kokkos::View<ScalarType ***, layout, exec_space>;
245 using IntViewType = Kokkos::View<int *, layout, exec_space>;
246 using KrylovHandleType = KokkosBatched::KrylovHandle<Norm2DViewType, IntViewType, Scalar3DViewType>;
247 const int n_iterations = maxit;
248 //const int team_size = -1;
249 const int vector_length = -1;
250 const double tol = rtol;
251 const int ortho_strategy = 0;
252 KrylovHandleType handle(Nsolves, Nsolves_team, n_iterations, true);
253 handle.Arnoldi_view = Scalar3DViewType("", Nsolves, n_iterations, Nloc + n_iterations + 3);
254 // solve
255 Functor_TestBatchedTeamVectorGMRES<exec_space, AMatrixValueView, IntView, XYType, KrylovHandleType>(batch_values, inv_diag, rowOffsets, colIndices, batch_x, batch_b, Nsolves_team, -1, vector_length, n_iterations, tol, ortho_strategy, 0, handle).run(pc);
256 Kokkos::fence();
257 // get data back
258 Kokkos::parallel_for(
259 "map", Kokkos::TeamPolicy<>(Nsolves /* /batch_sz */, -1, PCBJKOKKOS_VEC_SIZE), KOKKOS_LAMBDA(const team_member team) {
260 const int blkID = team.league_rank(), start = d_bid_eqOffset[blkID], end = d_bid_eqOffset[blkID + 1]; // 0
261 // map x into Plex/PETSc
262 Kokkos::parallel_for(Kokkos::TeamVectorRange(team, start, end), [=](int rowb) {
263 int rowa = d_isicol[rowb];
264 glb_xdata[rowa] = batch_x(blkID, rowb - start);
265 });
266 });
267 // output assume species major - clone from Kokkos solvers
268 #if PCBJKOKKOS_VERBOSE_LEVEL >= 3
269 #if PCBJKOKKOS_VERBOSE_LEVEL >= 4
270 PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), "Iterations\n"));
271 #else
272 PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), "max iterations per species (gmres) :"));
273 #endif
274 for (PetscInt dmIdx = 0, s = 0, head = 0; dmIdx < jac->num_dms; dmIdx += batch_sz) {
275 for (PetscInt f = 0, idx = head; f < jac->dm_Nf[dmIdx]; f++, s++, idx++) {
276 #if PCBJKOKKOS_VERBOSE_LEVEL >= 4
277 PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), "%2D:", s));
278 for (int bid = 0; bid < batch_sz; bid++) PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), "%3D ", handle.get_iteration_host(idx + bid * jac->dm_Nf[dmIdx])));
279 PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), "\n"));
280 #else
281 int count = 0, ii;
282 for (int bid = 0; bid < batch_sz; bid++) {
283 if ((ii = handle.get_iteration_host(idx + bid * jac->dm_Nf[dmIdx])) > count) count = ii;
284 }
285 PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), "%3d", count));
286 #endif
287 }
288 head += batch_sz * jac->dm_Nf[dmIdx];
289 }
290 #if PCBJKOKKOS_VERBOSE_LEVEL == 3
291 PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), "\n"));
292 #endif
293 #endif
294 // return error code, get max it
295 PetscInt count = 0, mbid = 0;
296 if (handle.is_converged_host()) {
297 *pcreason = PC_NOERROR;
298 if (!jac->max_nits) {
299 for (int blkID = 0; blkID < nBlk; blkID++) {
300 if (handle.get_iteration_host(blkID) > jac->max_nits) {
301 jac->max_nits = handle.get_iteration_host(blkID);
302 mbid = blkID;
303 }
304 }
305 }
306 } else {
307 PetscCall(PetscPrintf(PETSC_COMM_SELF, "There is at least one system that did not converge."));
308 *pcreason = PC_SUBPC_ERROR;
309 }
310 // output - assume species major order
311 for (int blkID = 0; blkID < nBlk; blkID++) {
312 if (jac->reason) { // -pc_bjkokkos_ksp_converged_reason
313 if (jac->batch_target == blkID) {
314 if (batch_sz != 1)
315 PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), " Linear solve %s in %d iterations, batch %" PetscInt_FMT ", species %" PetscInt_FMT "\n", handle.is_converged_host(blkID) ? "converged" : "diverged", handle.get_iteration_host(blkID), blkID % batch_sz, blkID / batch_sz));
316 else PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), " Linear solve %s in %d iterations, block %" PetscInt_FMT "\n", handle.is_converged_host(blkID) ? "converged" : "diverged", handle.get_iteration_host(blkID), blkID));
317 } else if (jac->batch_target == -1 && handle.get_iteration_host(blkID) >= count) {
318 jac->max_nits = count = handle.get_iteration_host(blkID);
319 mbid = blkID;
320 }
321 if (!handle.is_converged_host(blkID)) PetscCall(PetscPrintf(PETSC_COMM_SELF, "ERROR species %d, batch %d did not converge with %d iterations\n", (int)(blkID / batch_sz), (int)blkID % batch_sz, handle.get_iteration_host(blkID)));
322 }
323 }
324 if (jac->batch_target == -1 && jac->reason) {
325 if (batch_sz != 1)
326 PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), " Linear solve %s in %d iteration, batch %" PetscInt_FMT ", specie %" PetscInt_FMT "\n", handle.is_converged_host(mbid) ? "converged" : "diverged", jac->max_nits, mbid % batch_sz, mbid / batch_sz));
327 else PetscCall(PetscPrintf(PetscObjectComm((PetscObject)A), " Linear solve %s in %d iteration, block %" PetscInt_FMT "\n", handle.is_converged_host(mbid) ? "converged" : "diverged", jac->max_nits, mbid));
328 }
329 #if defined(PETSC_HAVE_CUDA)
330 nvtxRangePop();
331 #endif
332
333 return PETSC_SUCCESS;
334 }
335 #endif
336