// Check if there are any JVM TI prefixes which have been applied to the native method name.
// If any are found, remove them before attemping the look up of the
// native implementation again.
// See SetNativeMethodPrefix in the JVM TI Spec for more details.
address NativeLookup::lookup_entry_prefixed(methodHandle method, bool& in_base_library, TRAPS) {
ResourceMark rm(THREAD);
int prefix_count;
char** prefixes = JvmtiExport::get_all_native_method_prefixes(&prefix_count);
char* in_name = method->name()->as_C_string();
char* wrapper_name = in_name;
// last applied prefix will be first -- go backwards
for (int i = prefix_count-1; i >= 0; i--) {
char* prefix = prefixes[i];
size_t prefix_len = strlen(prefix);
if (strncmp(prefix, wrapper_name, prefix_len) == 0) {
// has this prefix remove it
wrapper_name += prefix_len;
}
}
if (wrapper_name != in_name) {
// we have a name for a wrapping method
int wrapper_name_len = (int)strlen(wrapper_name);
TempNewSymbol wrapper_symbol = SymbolTable::probe(wrapper_name, wrapper_name_len);
if (wrapper_symbol != NULL) {
KlassHandle kh(method->method_holder());
methodOop wrapper_method = Klass::cast(kh())->lookup_method(wrapper_symbol,
method->signature());
if (wrapper_method != NULL && !wrapper_method->is_native()) {
// we found a wrapper method, use its native entry
method->set_is_prefixed_native();
return lookup_entry(wrapper_method, in_base_library, THREAD);
}
}
}
return NULL;
}
klassVtable* arrayKlass::vtable() const {
KlassHandle kh(Thread::current(), as_klassOop());
return new klassVtable(kh, start_of_vtable(), vtable_length() / vtableEntry::size());
}
void KernelRegister::add(cl_cpd::Kernel* kernel)
{
KernelHandler kh(kernel);
m[kh->getName()] = kh;
}
void omxComputeNumericDeriv::computeImpl(FitContext *fc)
{
if (fc->fitUnits == FIT_UNITS_SQUARED_RESIDUAL ||
fc->fitUnits == FIT_UNITS_SQUARED_RESIDUAL_CHISQ) { // refactor TODO
numParams = 0;
if (verbose >= 1) mxLog("%s: derivatives %s units are meaningless",
name, fitUnitsToName(fc->fitUnits));
return; //Possible TODO: calculate Hessian anyway?
}
int newWanted = fc->wanted | FF_COMPUTE_GRADIENT;
if (wantHessian) newWanted |= FF_COMPUTE_HESSIAN;
int nf = fc->calcNumFree();
if (numParams != 0 && numParams != nf) {
mxThrow("%s: number of parameters changed from %d to %d",
name, numParams, nf);
}
numParams = nf;
if (numParams <= 0) { complainNoFreeParam(); return; }
optima.resize(numParams);
fc->copyEstToOptimizer(optima);
paramMap.resize(numParams);
for (int px=0,ex=0; px < numParams; ++ex) {
if (fc->profiledOut[ex]) continue;
paramMap[px++] = ex;
}
omxAlgebraPreeval(fitMat, fc);
fc->createChildren(fitMat); // allow FIML rowwiseParallel even when parallel=false
fc->state->countNonlinearConstraints(fc->state->numEqC, fc->state->numIneqC, false);
int c_n = fc->state->numEqC + fc->state->numIneqC;
fc->constraintFunVals.resize(c_n);
fc->constraintJacobian.resize(c_n, numParams);
if(c_n){
omxCalcFinalConstraintJacobian(fc, numParams);
}
// TODO: Allow more than one hessian value for calculation
int numChildren = 1;
if (parallel && !fc->openmpUser && fc->childList.size()) numChildren = fc->childList.size();
if (!fc->haveReferenceFit(fitMat)) return;
minimum = fc->fit;
hessWorkVector = new hess_struct[numChildren];
if (numChildren == 1) {
omxPopulateHessianWork(hessWorkVector, fc);
} else {
for(int i = 0; i < numChildren; i++) {
omxPopulateHessianWork(hessWorkVector + i, fc->childList[i]);
}
}
if(verbose >= 1) mxLog("Numerical Hessian approximation (%d children, ref fit %.2f)",
numChildren, minimum);
hessian = NULL;
if (wantHessian) {
hessian = fc->getDenseHessUninitialized();
Eigen::Map< Eigen::MatrixXd > eH(hessian, numParams, numParams);
eH.setConstant(NA_REAL);
if (knownHessian) {
int khSize = int(khMap.size());
Eigen::Map< Eigen::MatrixXd > kh(knownHessian, khSize, khMap.size());
for (int rx=0; rx < khSize; ++rx) {
for (int cx=0; cx < khSize; ++cx) {
if (khMap[rx] < 0 || khMap[cx] < 0) continue;
eH(khMap[rx], khMap[cx]) = kh(rx, cx);
}
}
}
}
if (detail) {
recordDetail = false; // already done it once
} else {
Rf_protect(detail = Rf_allocVector(VECSXP, 4));
SET_VECTOR_ELT(detail, 0, Rf_allocVector(LGLSXP, numParams));
for (int gx=0; gx < 3; ++gx) {
SET_VECTOR_ELT(detail, 1+gx, Rf_allocVector(REALSXP, numParams));
}
SEXP detailCols;
Rf_protect(detailCols = Rf_allocVector(STRSXP, 4));
Rf_setAttrib(detail, R_NamesSymbol, detailCols);
SET_STRING_ELT(detailCols, 0, Rf_mkChar("symmetric"));
SET_STRING_ELT(detailCols, 1, Rf_mkChar("forward"));
SET_STRING_ELT(detailCols, 2, Rf_mkChar("central"));
SET_STRING_ELT(detailCols, 3, Rf_mkChar("backward"));
SEXP detailRowNames;
Rf_protect(detailRowNames = Rf_allocVector(STRSXP, numParams));
Rf_setAttrib(detail, R_RowNamesSymbol, detailRowNames);
for (int nx=0; nx < int(numParams); ++nx) {
SET_STRING_ELT(detailRowNames, nx, Rf_mkChar(fc->varGroup->vars[nx]->name));
}
markAsDataFrame(detail);
}
gforward = REAL(VECTOR_ELT(detail, 1));
gcentral = REAL(VECTOR_ELT(detail, 2));
gbackward = REAL(VECTOR_ELT(detail, 3));
Eigen::Map< Eigen::ArrayXd > Gf(gforward, numParams);
Eigen::Map< Eigen::ArrayXd > Gc(gcentral, numParams);
Eigen::Map< Eigen::ArrayXd > Gb(gbackward, numParams);
Gf.setConstant(NA_REAL);
Gc.setConstant(NA_REAL);
Gb.setConstant(NA_REAL);
calcHessianEntry che(this);
CovEntrywiseParallel(numChildren, che);
for(int i = 0; i < numChildren; i++) {
struct hess_struct *hw = hessWorkVector + i;
totalProbeCount += hw->probeCount;
}
delete [] hessWorkVector;
if (isErrorRaised()) return;
Eigen::Map< Eigen::ArrayXi > Gsymmetric(LOGICAL(VECTOR_ELT(detail, 0)), numParams);
double gradNorm = 0.0;
double feasibilityTolerance = Global->feasibilityTolerance;
for (int px=0; px < numParams; ++px) {
// factor out simliar code in ComputeNR
omxFreeVar &fv = *fc->varGroup->vars[ paramMap[px] ];
if ((fabs(optima[px] - fv.lbound) < feasibilityTolerance && Gc[px] > 0) ||
(fabs(optima[px] - fv.ubound) < feasibilityTolerance && Gc[px] < 0)) {
Gsymmetric[px] = false;
continue;
}
gradNorm += Gc[px] * Gc[px];
double relsym = 2 * fabs(Gf[px] + Gb[px]) / (Gb[px] - Gf[px]);
Gsymmetric[px] = (Gf[px] < 0 && 0 < Gb[px] && relsym < 1.5);
if (checkGradient && verbose >= 2 && !Gsymmetric[px]) {
mxLog("%s: param[%d] %d %f", name, px, Gsymmetric[px], relsym);
}
}
fc->grad.resize(fc->numParam);
fc->grad.setZero();
fc->copyGradFromOptimizer(Gc);
if(c_n){
fc->inequality.resize(fc->state->numIneqC);
fc->analyticIneqJacTmp.resize(fc->state->numIneqC, numParams);
fc->myineqFun(true, verbose, omxConstraint::LESS_THAN, false);
}
gradNorm = sqrt(gradNorm);
double gradThresh = Global->getGradientThreshold(minimum);
//The gradient will generally not be near zero at a local minimum if there are equality constraints
//or active inequality constraints:
if ( checkGradient && gradNorm > gradThresh && !(fc->state->numEqC || fc->inequality.array().sum()) ) {
if (verbose >= 1) {
mxLog("Some gradient entries are too large, norm %f", gradNorm);
}
if (fc->getInform() < INFORM_NOT_AT_OPTIMUM) fc->setInform(INFORM_NOT_AT_OPTIMUM);
}
fc->setEstFromOptimizer(optima);
// auxillary information like per-row likelihoods need a refresh
ComputeFit(name, fitMat, FF_COMPUTE_FIT, fc);
fc->wanted = newWanted;
}