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Remove unnecessary memory allocations when using SuiteSparse.

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1. Add SuiteSparse::CreateDenseVectorView
2. Replace calls to SuiteSparse::CreateDenseVector with
   SuiteSparse::CreateDenseVectorView.
2. Replace NULL with nullptr in suitesparse.cc and
   dynamic_sparse_normal_cholesky_solver.cc

Change-Id: I94355c1dc27789e5b987a7b2850e9db6176a0914
This commit is contained in:
Sameer Agarwal
2018-04-09 13:52:38 -07:00
parent 21ec8f462c
commit 83f70e5c02
3 changed files with 47 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files
internal/ceres/dynamic_sparse_normal_cholesky_solver.cc
+7 -8
View File
@@ -66,7 +66,7 @@ LinearSolver::Summary DynamicSparseNormalCholeskySolver::SolveImpl(
VectorRef(x, num_cols).setZero();
A->LeftMultiply(b, x);
if (per_solve_options.D != NULL) {
if (per_solve_options.D != nullptr) {
// Temporarily append a diagonal block to the A matrix, but undo
// it before returning the matrix to the user.
std::unique_ptr<CompressedRowSparseMatrix> regularizer;
@@ -96,7 +96,7 @@ LinearSolver::Summary DynamicSparseNormalCholeskySolver::SolveImpl(
<< options_.sparse_linear_algebra_library_type;
}
if (per_solve_options.D != NULL) {
if (per_solve_options.D != nullptr) {
A->DeleteRows(num_cols);
}
@@ -253,19 +253,18 @@ DynamicSparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
cholmod_factor* factor = ss.AnalyzeCholesky(&lhs, &summary.message);
event_logger.AddEvent("Analysis");
if (factor == NULL) {
if (factor == nullptr) {
summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
return summary;
}
summary.termination_type = ss.Cholesky(&lhs, factor, &summary.message);
if (summary.termination_type == LINEAR_SOLVER_SUCCESS) {
cholmod_dense* rhs =
ss.CreateDenseVector(rhs_and_solution, num_cols, num_cols);
cholmod_dense* solution = ss.Solve(factor, rhs, &summary.message);
cholmod_dense cholmod_rhs =
ss.CreateDenseVectorView(rhs_and_solution, num_cols);
cholmod_dense* solution = ss.Solve(factor, &cholmod_rhs, &summary.message);
event_logger.AddEvent("Solve");
ss.Free(rhs);
if (solution != NULL) {
if (solution != nullptr) {
memcpy(
rhs_and_solution, solution->x, num_cols * sizeof(*rhs_and_solution));
ss.Free(solution);
internal/ceres/suitesparse.cc
+35 -24
View File
@@ -97,11 +97,11 @@ cholmod_sparse SuiteSparse::CreateSparseMatrixTransposeView(
m.nrow = A->num_cols();
m.ncol = A->num_rows();
m.nzmax = A->num_nonzeros();
m.nz = NULL;
m.nz = nullptr;
m.p = reinterpret_cast<void*>(A->mutable_rows());
m.i = reinterpret_cast<void*>(A->mutable_cols());
m.x = reinterpret_cast<void*>(A->mutable_values());
m.z = NULL;
m.z = nullptr;
if (A->storage_type() == CompressedRowSparseMatrix::LOWER_TRIANGULAR) {
m.stype = 1;
@@ -120,12 +120,24 @@ cholmod_sparse SuiteSparse::CreateSparseMatrixTransposeView(
return m;
}
cholmod_dense SuiteSparse::CreateDenseVectorView(const double* x, int size) {
cholmod_dense v;
v.nrow = size;
v.ncol = 1;
v.nzmax = size;
v.d = size;
v.x = const_cast<void*>(reinterpret_cast<const void*>(x));
v.xtype = CHOLMOD_REAL;
v.dtype = CHOLMOD_DOUBLE;
return v;
}
cholmod_dense* SuiteSparse::CreateDenseVector(const double* x,
int in_size,
int out_size) {
CHECK_LE(in_size, out_size);
cholmod_dense* v = cholmod_zeros(out_size, 1, CHOLMOD_REAL, &cc_);
if (x != NULL) {
if (x != nullptr) {
memcpy(v->x, x, in_size * sizeof(*x));
}
return v;
@@ -149,7 +161,7 @@ cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A,
if (cc_.status != CHOLMOD_OK) {
*message =
StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
return NULL;
return nullptr;
}
return CHECK_NOTNULL(factor);
@@ -161,7 +173,7 @@ cholmod_factor* SuiteSparse::BlockAnalyzeCholesky(cholmod_sparse* A,
string* message) {
vector<int> ordering;
if (!BlockAMDOrdering(A, row_blocks, col_blocks, &ordering)) {
return NULL;
return nullptr;
}
return AnalyzeCholeskyWithUserOrdering(A, ordering, message);
}
@@ -174,14 +186,14 @@ cholmod_factor* SuiteSparse::AnalyzeCholeskyWithUserOrdering(
cc_.method[0].ordering = CHOLMOD_GIVEN;
cholmod_factor* factor =
cholmod_analyze_p(A, const_cast<int*>(&ordering[0]), NULL, 0, &cc_);
cholmod_analyze_p(A, const_cast<int*>(&ordering[0]), nullptr, 0, &cc_);
if (VLOG_IS_ON(2)) {
cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
}
if (cc_.status != CHOLMOD_OK) {
*message =
StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
return NULL;
return nullptr;
}
return CHECK_NOTNULL(factor);
@@ -200,7 +212,7 @@ cholmod_factor* SuiteSparse::AnalyzeCholeskyWithNaturalOrdering(
if (cc_.status != CHOLMOD_OK) {
*message =
StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
return NULL;
return nullptr;
}
return CHECK_NOTNULL(factor);
@@ -230,7 +242,7 @@ bool SuiteSparse::BlockAMDOrdering(const cholmod_sparse* A,
block_matrix.nzmax = block_rows.size();
block_matrix.p = reinterpret_cast<void*>(&block_cols[0]);
block_matrix.i = reinterpret_cast<void*>(&block_rows[0]);
block_matrix.x = NULL;
block_matrix.x = nullptr;
block_matrix.stype = A->stype;
block_matrix.itype = CHOLMOD_INT;
block_matrix.xtype = CHOLMOD_PATTERN;
@@ -239,7 +251,7 @@ bool SuiteSparse::BlockAMDOrdering(const cholmod_sparse* A,
block_matrix.packed = 1;
vector<int> block_ordering(num_row_blocks);
if (!cholmod_amd(&block_matrix, NULL, 0, &block_ordering[0], &cc_)) {
if (!cholmod_amd(&block_matrix, nullptr, 0, &block_ordering[0], &cc_)) {
return false;
}
@@ -311,7 +323,7 @@ cholmod_dense* SuiteSparse::Solve(cholmod_factor* L,
string* message) {
if (cc_.status != CHOLMOD_OK) {
*message = "cholmod_solve failed. CHOLMOD status is not CHOLMOD_OK";
return NULL;
return nullptr;
}
return cholmod_solve(CHOLMOD_A, L, b, &cc_);
@@ -319,13 +331,13 @@ cholmod_dense* SuiteSparse::Solve(cholmod_factor* L,
bool SuiteSparse::ApproximateMinimumDegreeOrdering(cholmod_sparse* matrix,
int* ordering) {
return cholmod_amd(matrix, NULL, 0, ordering, &cc_);
return cholmod_amd(matrix, nullptr, 0, ordering, &cc_);
}
bool SuiteSparse::ConstrainedApproximateMinimumDegreeOrdering(
cholmod_sparse* matrix, int* constraints, int* ordering) {
#ifndef CERES_NO_CAMD
return cholmod_camd(matrix, NULL, 0, constraints, ordering, &cc_);
return cholmod_camd(matrix, nullptr, 0, constraints, ordering, &cc_);
#else
LOG(FATAL) << "Congratulations you have found a bug in Ceres."
<< "Ceres Solver was compiled with SuiteSparse "
@@ -342,24 +354,24 @@ SuiteSparseCholesky* SuiteSparseCholesky::Create(
}
SuiteSparseCholesky::SuiteSparseCholesky(const OrderingType ordering_type)
: ordering_type_(ordering_type), factor_(NULL) {}
: ordering_type_(ordering_type), factor_(nullptr) {}
SuiteSparseCholesky::~SuiteSparseCholesky() {
if (factor_ != NULL) {
if (factor_ != nullptr) {
ss_.Free(factor_);
}
}
LinearSolverTerminationType SuiteSparseCholesky::Factorize(
CompressedRowSparseMatrix* lhs, string* message) {
if (lhs == NULL) {
if (lhs == nullptr) {
*message = "Failure: Input lhs is NULL.";
return LINEAR_SOLVER_FATAL_ERROR;
}
cholmod_sparse cholmod_lhs = ss_.CreateSparseMatrixTransposeView(lhs);
if (factor_ == NULL) {
if (factor_ == nullptr) {
if (ordering_type_ == NATURAL) {
factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(&cholmod_lhs, message);
} else {
@@ -371,7 +383,7 @@ LinearSolverTerminationType SuiteSparseCholesky::Factorize(
}
}
if (factor_ == NULL) {
if (factor_ == nullptr) {
return LINEAR_SOLVER_FATAL_ERROR;
}
}
@@ -390,18 +402,17 @@ LinearSolverTerminationType SuiteSparseCholesky::Solve(const double* rhs,
double* solution,
string* message) {
// Error checking
if (factor_ == NULL) {
if (factor_ == nullptr) {
*message = "Solve called without a call to Factorize first.";
return LINEAR_SOLVER_FATAL_ERROR;
}
const int num_cols = factor_->n;
cholmod_dense* cholmod_dense_rhs =
ss_.CreateDenseVector(rhs, num_cols, num_cols);
cholmod_dense cholmod_rhs = ss_.CreateDenseVectorView(rhs, num_cols);
cholmod_dense* cholmod_dense_solution =
ss_.Solve(factor_, cholmod_dense_rhs, message);
ss_.Free(cholmod_dense_rhs);
if (cholmod_dense_solution == NULL) {
ss_.Solve(factor_, &cholmod_rhs, message);
if (cholmod_dense_solution == nullptr) {
return LINEAR_SOLVER_FAILURE;
}
internal/ceres/suitesparse.h
+5
View File
@@ -99,6 +99,11 @@ class SuiteSparse {
// use the SuiteSparse machinery to allocate memory.
cholmod_sparse CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A);
// Create a cholmod_dense vector around the contents of the array x.
// This is a shallow object, which refers to the contents of x and
// does not use the SuiteSparse machinery to allocate memory.
cholmod_dense CreateDenseVectorView(const double* x, int size);
// Given a vector x, build a cholmod_dense vector of size out_size
// with the first in_size entries copied from x. If x is NULL, then
// an all zeros vector is returned. Caller owns the result.