wehub-resource-sync
634 lines
25 KiB
C++
634 lines
25 KiB
C++
/* ******************************************************************************
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*
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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//
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#include <array/NDArrayFactory.h>
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#include <execution/Threads.h>
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#include <helpers/MmulHelper.h>
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#include <ops/declarable/helpers/top_k.h>
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#if NOT_EXCLUDED(OP_lup)
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namespace sd {
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namespace ops {
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namespace helpers {
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template <typename T>
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static void swapRows_(NDArray* matrix, sd::LongType theFirst, sd::LongType theSecond) {
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if (theFirst != theSecond)
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for (sd::LongType i = 0; i < matrix->columns(); i++) {
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math::sd_swap(matrix->r<T>(theFirst, i), matrix->r<T>(theSecond, i));
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}
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}
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BUILD_SINGLE_TEMPLATE( void swapRows_, (NDArray * matrix, sd::LongType theFirst, sd::LongType theSecond), SD_FLOAT_TYPES);
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template <typename T>
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static void swapRows(T* matrixBuf, sd::LongType const* matrixShape, sd::LongType theFirst, sd::LongType theSecond) {
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if (theFirst != theSecond) {
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auto n = shape::sizeAt(matrixShape, static_cast<sd::LongType>(-1));
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auto loop = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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sd::LongType theFirstPos[] = {theFirst, i};
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sd::LongType theSecondPos[] = {theSecond, i};
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sd::LongType theFirstIndex;
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COORDS2INDEX(shape::rank(matrixShape), shape::stride(matrixShape), theFirstPos, theFirstIndex);
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sd::LongType theSecondIndex;
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COORDS2INDEX(shape::rank(matrixShape), shape::stride(matrixShape), theSecondPos, theSecondIndex);
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math::sd_swap(matrixBuf[theFirstIndex], matrixBuf[theSecondIndex]);
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}
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};
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samediff::Threads::parallel_tad(loop, 0, n, 1);
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}
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}
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void swapRows(NDArray* matrix, sd::LongType theFirst, sd::LongType theSecond) {
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BUILD_SINGLE_SELECTOR(matrix->dataType(), swapRows_, (matrix, theFirst, theSecond), SD_FLOAT_TYPES);
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}
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template <typename T>
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static void invertLowerMatrix_(NDArray* inputMatrix, NDArray* invertedMatrix) {
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sd::LongType n = inputMatrix->rows();
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invertedMatrix->setIdentity();
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if (inputMatrix->isIdentityMatrix()) return;
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auto invertDiagonals = PRAGMA_THREADS_FOR {
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for (sd::LongType i = start; i < stop; i += increment) invertedMatrix->r<T>(i, i) /= inputMatrix->t<T>(i, i);
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};
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auto invertSubDiagonals = PRAGMA_THREADS_FOR {
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for (sd::LongType i = start; i < stop; i += increment)
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invertedMatrix->r<T>(i, i - 1) -=
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(inputMatrix->t<T>(i, i - 1) * invertedMatrix->t<T>(i - 1, i - 1) / inputMatrix->t<T>(i, i));
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};
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samediff::Threads::parallel_for(invertDiagonals, 0, n, 1);
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samediff::Threads::parallel_for(invertSubDiagonals, 1, n, 1);
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for (sd::LongType i = 1; i < n; i++) {
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for (sd::LongType j = 0; j < i - 1; j++)
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for (sd::LongType k = 0; k < i; k++)
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invertedMatrix->r<T>(i, j) -=
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((invertedMatrix->t<T>(k, j) * inputMatrix->t<T>(i, k) / inputMatrix->t<T>(i, i)));
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}
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}
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BUILD_SINGLE_TEMPLATE( void invertLowerMatrix_, (NDArray * inputMatrix, NDArray* invertedMatrix);
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, SD_FLOAT_TYPES);
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void invertLowerMatrix(NDArray* inputMatrix, NDArray* invertedMatrix) {
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BUILD_SINGLE_SELECTOR(inputMatrix->dataType(), invertLowerMatrix_, (inputMatrix, invertedMatrix), SD_FLOAT_TYPES);
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}
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template <typename T>
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static void _invertUpperMatrix(NDArray* inputMatrix, NDArray* invertedMatrix) {
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sd::LongType n = inputMatrix->rows();
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invertedMatrix->setIdentity();
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if (inputMatrix->isIdentityMatrix()) { // the inverse for I is I
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return;
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}
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auto invertDiagonals = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) invertedMatrix->r<T>(i, i) /= inputMatrix->t<T>(i, i);
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};
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// PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance().elementwiseThreshold())
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auto invertUpDiagonals = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment)
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invertedMatrix->r<T>(i, i + 1) -=
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(inputMatrix->t<T>(i, i + 1) * invertedMatrix->t<T>(i + 1, i + 1) / inputMatrix->t<T>(i, i));
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};
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samediff::Threads::parallel_for(invertDiagonals, 0, n, 1);
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samediff::Threads::parallel_for(invertUpDiagonals, 0, n - 1, 1);
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for (auto i = n - 2; i >= 0; i--) {
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for (auto j = i + 2; j < n; j++)
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for (auto k = i; k < n; k++)
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invertedMatrix->r<T>(i, j) -=
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((invertedMatrix->t<T>(k, j) * inputMatrix->t<T>(i, k) / inputMatrix->t<T>(i, i)));
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}
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}
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BUILD_SINGLE_TEMPLATE( void _invertUpperMatrix, (NDArray * inputMatrix, NDArray* invertedMatrix);
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, SD_FLOAT_TYPES);
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void invertUpperMatrix(NDArray* inputMatrix, NDArray* invertedMatrix) {
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BUILD_SINGLE_SELECTOR(inputMatrix->dataType(), _invertUpperMatrix, (inputMatrix, invertedMatrix), SD_FLOAT_TYPES);
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}
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template <typename T, typename I>
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static NDArray lup_(LaunchContext* context, NDArray* input, NDArray* compound, NDArray* permutation) {
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const sd::LongType rowNum = input->rows();
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const sd::LongType columnNum = input->columns();
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// FIXED: Use stack allocation instead of heap to avoid memory leak
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NDArray determinant(DataTypeUtils::fromT<T>(), context, true); // scalar initialized to 0
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determinant.p<T>(0, static_cast<T>(1.f)); // set value to 1
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NDArray compoundMatrix = *input; // copy
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NDArray permutationMatrix(input, false, context); // has same shape as input and contiguous strides
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permutationMatrix.setIdentity();
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T pivotValue; // = T(0.0);
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sd::LongType pivot; // = -1;
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sd::LongType swapCount = 0;
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for (sd::LongType i = 0; i < rowNum; i++) {
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pivotValue = T(0.0);
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pivot = -1;
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for (sd::LongType rowCounter = i; rowCounter < rowNum; rowCounter++) {
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if (sd::math::sd_abs<T,T>(compoundMatrix.t<T>(rowCounter, i)) > pivotValue) {
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pivotValue = sd::math::sd_abs<T,T>(compoundMatrix.t<T>(rowCounter, i));
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pivot = rowCounter;
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}
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}
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if (pivotValue > DataTypeUtils::min_positive<T>()) {
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swapRows(&compoundMatrix, pivot, i);
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swapRows(&permutationMatrix, pivot, i);
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if (pivot != i) swapCount++;
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for (sd::LongType j = i + 1; j < rowNum; j++) {
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compoundMatrix.r<T>(j, i) /= compoundMatrix.t<T>(i, i);
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for (sd::LongType k = i + 1; k < rowNum; k++) {
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compoundMatrix.r<T>(j, k) -= compoundMatrix.t<T>(j, i) * compoundMatrix.t<T>(i, k);
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}
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}
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}
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}
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for (sd::LongType e = 0; e < rowNum; e++) {
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determinant.p<T>(0, determinant.e<T>(0) * compoundMatrix.e<T>(e, e));
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}
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if (swapCount % 2) {
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determinant.p<T>(0, -determinant.e<T>(0));
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}
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if (compound != nullptr) compound->assign(&compoundMatrix);
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if (permutation != nullptr) {
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auto permutaionVector = NDArrayFactory::create('c', {rowNum}, DataTypeUtils::fromT<I>(), input->getContext());
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for (auto i = 0; i < rowNum; i++) {
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for (auto j = 0; j < columnNum; j++) {
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if (permutationMatrix.t<T>(i, j) != 0) {
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permutaionVector->template r<I>(i) = j;
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}
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}
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}
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if (permutationMatrix.isSameShape(permutation))
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permutation->assign(&permutationMatrix);
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else if (permutation->isSameShape(permutaionVector)) {
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permutation->assign(permutaionVector);
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}
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}
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return determinant; // FIXED: Return stack-allocated object instead of dereferencing pointer
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}
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BUILD_DOUBLE_TEMPLATE( NDArray lup_,
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(LaunchContext * context, NDArray* input, NDArray* output, NDArray* permutation), SD_FLOAT_TYPES,
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SD_INDEXING_TYPES);
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/*
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* lu decomposition with naive algorithm with partial pivoting
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* */
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template <typename T, typename I>
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static I argmaxCol(I column, T* compoundBuffer, sd::LongType const* compoundShape) {
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auto rowNum = shape::sizeAt(compoundShape, static_cast<sd::LongType>(0));
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sd::LongType xInitial[] = {column, column};
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sd::LongType xInitialIndex;
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COORDS2INDEX(shape::rank(compoundShape), shape::stride(compoundShape), xInitial, xInitialIndex);
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auto maxValue = T(0);
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auto result = -1;
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auto start = column;
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auto stop = rowNum;
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auto increment = 1;
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for (auto rowCounter = start; rowCounter < stop; rowCounter++) {
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sd::LongType xPos[] = {rowCounter, column};
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sd::LongType xIndex;
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COORDS2INDEX(shape::rank(compoundShape), shape::stride(compoundShape), xPos, xIndex);
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if (sd::math::sd_abs<T,T>(compoundBuffer[xIndex]) > maxValue) {
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maxValue = sd::math::sd_max(maxValue, sd::math::sd_abs<T,T>(compoundBuffer[xIndex]));
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result = rowCounter;
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}
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}
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return result;
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}
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template <typename T>
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void processColumns(sd::LongType currentRow, sd::LongType rowNum, T* compoundBuf, sd::LongType const* compoundShape) {
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sd::LongType xDiag[] = {currentRow, currentRow};
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sd::LongType diagIndex;
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COORDS2INDEX(shape::rank(compoundShape), shape::stride(compoundShape), xDiag, diagIndex);
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auto loop = PRAGMA_THREADS_FOR {
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for (auto j = start; j < stop; j++) {
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sd::LongType xRow[] = {j, currentRow};
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sd::LongType rowIndex;
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COORDS2INDEX(shape::rank(compoundShape), shape::stride(compoundShape), xRow, rowIndex);
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compoundBuf[rowIndex] /= compoundBuf[diagIndex]; // output->t<T>(i, i);
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for (sd::LongType k = currentRow + 1; k < rowNum; k++) {
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sd::LongType yRow[] = {j, k};
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sd::LongType yCol[] = {currentRow, k};
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sd::LongType rowIndexY, colIndex;
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COORDS2INDEX(shape::rank(compoundShape), shape::stride(compoundShape), yRow, rowIndexY);
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COORDS2INDEX(shape::rank(compoundShape), shape::stride(compoundShape), yCol, colIndex);
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compoundBuf[rowIndexY] -= compoundBuf[rowIndex] * compoundBuf[colIndex];
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}
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}
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};
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samediff::Threads::parallel_tad(loop, currentRow + 1, rowNum, 1);
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}
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template <typename T>
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static void doolitleLU(LaunchContext* context, NDArray* compound, sd::LongType rowNum) {
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auto input = compound->dup();
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compound->nullify();
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// Decomposing matrix into Upper and Lower
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// triangular matrix
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for (auto i = 0; i < rowNum; i++) {
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// Upper Triangular
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for (auto k = i; k < rowNum; k++) {
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// Summation of L(i, j) * U(j, k)
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sd::LongType sum = 0;
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for (sd::LongType j = 0; j < i; j++) sum += compound->t<T>(i, j) * compound->t<T>(j, k);
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// Evaluating U(i, k)
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compound->r<T>(i, k) = input->t<T>(i, k) - sum;
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}
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// Lower Triangular
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for (sd::LongType k = i + 1; k < rowNum; k++) {
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// Summation of L(k, j) * U(j, i)
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sd::LongType sum = 0;
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for (sd::LongType j = 0; j < i; j++) sum += compound->t<T>(k, j) * compound->t<T>(j, i);
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// Evaluating L(k, i)
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compound->r<T>(k, i) = (input->t<T>(k, i) - sum) / compound->t<T>(i, i);
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}
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}
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delete input; // Clean up duped array
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}
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template <typename T, typename I>
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static void luNN_(LaunchContext* context, NDArray* compound, NDArray* permutation, sd::LongType rowNum) {
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if (permutation) { // LUP algorithm
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// Initialize permutation array
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permutation->linspace(0);
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// Cache all buffers and shape data upfront
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auto permutationBuf = permutation->bufferAsT<I>();
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auto compoundBuf = compound->bufferAsT<T>();
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auto compoundShape = compound->shapeInfo();
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auto permutationShape = permutation->shapeInfo();
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// Cache shape-related values outside the main loop
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const int permRank = shape::rank(permutationShape);
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const sd::LongType* permShape = shape::shapeOf(permutationShape);
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const sd::LongType* permStride = shape::stride(permutationShape);
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// Main LU decomposition loop
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for (sd::LongType i = 0; i < rowNum - 1; i++) {
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auto pivotIndex = argmaxCol(i, compoundBuf, compoundShape);
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if (pivotIndex < 0) {
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THROW_EXCEPTION("helpers::luNN_: input matrix is singular.");
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}
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// Use cached shape values for coordinate transforms
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sd::LongType firstIndexCoords[SD_MAX_RANK];
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sd::LongType secondIndexCoords[SD_MAX_RANK];
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sd::LongType firstIndex;
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sd::LongType secondIndex;
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// Transform coordinates using cached shape data
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INDEX2COORDS(i, permRank, permShape, firstIndexCoords);
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COORDS2INDEX(permRank, permStride, firstIndexCoords, firstIndex);
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INDEX2COORDS(pivotIndex, permRank, permShape, secondIndexCoords);
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COORDS2INDEX(permRank, permStride, secondIndexCoords, secondIndex);
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// Perform the swaps
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math::sd_swap(permutationBuf[firstIndex], permutationBuf[secondIndex]);
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swapRows(compoundBuf, compoundShape, i, pivotIndex);
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// Process remaining columns
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processColumns(i, rowNum, compoundBuf, compoundShape);
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}
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} else { // Doolitle algorithm with LU decomposition
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doolitleLU<T>(context, compound, rowNum);
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}
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}
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template <typename T, typename I>
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static void lu_(LaunchContext* context, NDArray* input, NDArray* output, NDArray* permutationVectors) {
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auto n = input->sizeAt(-1);
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output->assign(input); // fill up output tensor with zeros
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ResultSet outputs = output->allTensorsAlongDimension({-2, -1});
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ResultSet permutations;
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if (permutationVectors) permutations = permutationVectors->allTensorsAlongDimension({-1});
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auto loop = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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luNN_<T, I>(context, outputs.at(i), permutationVectors ? permutations.at(i) : nullptr, n);
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}
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};
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samediff::Threads::parallel_for(loop, 0, outputs.size(), 1);
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}
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void lu(LaunchContext* context, NDArray* input, NDArray* output, NDArray* permutation) {
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BUILD_DOUBLE_SELECTOR(input->dataType(), permutation ? permutation->dataType() : DataType::INT32, lu_,
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(context, input, output, permutation), SD_FLOAT_TYPES, SD_INDEXING_TYPES);
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}
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template <typename T>
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static sd::Status determinant_(LaunchContext* context, NDArray* input, NDArray* output) {
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sd::LongType n = input->sizeAt(-1);
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sd::LongType n2 = n * n;
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auto matrix =
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NDArrayFactory::create(input->ordering(), {n, n}, input->dataType(), context); //, block.getWorkspace());
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for (sd::LongType e = 0; e < output->lengthOf(); e++) {
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for (sd::LongType k = e * n2, row = 0; k < (e + 1) * n2; ++k, ++row) matrix->p(row, input->e<T>(k));
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auto ret = lup_<T, sd::LongType>(context, matrix, (NDArray*)nullptr, (NDArray*)nullptr);
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output->p(e, &ret);
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}
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return sd::Status::OK;
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}
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sd::Status determinant(sd::LaunchContext* context, NDArray* input, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), return determinant_, (context, input, output), SD_FLOAT_TYPES);
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}
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template <typename T>
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sd::Status logAbsDeterminant_(LaunchContext* context, NDArray* input, NDArray* output) {
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sd::LongType n = input->sizeAt(-1);
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sd::LongType n2 = n * n;
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NDArray *matrix =
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NDArrayFactory::create(input->ordering(), {n, n}, input->dataType(), context); //, block.getWorkspace());
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for (sd::LongType e = 0; e < output->lengthOf(); e++) {
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for (sd::LongType k = e * n2, row = 0; k < (e + 1) * n2; ++k, ++row) {
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matrix->p(row, input->e<T>(k));
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}
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NDArray det = lup_<T, sd::LongType>(context, matrix, (NDArray*)nullptr, (NDArray*)nullptr);
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if (det.e<T>(0) != 0.f) output->p(e, sd::math::sd_log<T, T>(sd::math::sd_abs<T,T>(det.t<T>(0))));
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}
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delete matrix;
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return sd::Status::OK;
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}
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sd::Status logAbsDeterminant(sd::LaunchContext* context, NDArray* input, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), return logAbsDeterminant_, (context, input, output), SD_FLOAT_TYPES);
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}
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template <typename T>
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static sd::Status inverse_(LaunchContext* context, NDArray* input, NDArray* output) {
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auto n = input->sizeAt(-1);
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auto n2 = n * n;
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auto totalCount = output->lengthOf() / n2;
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float zerof = 0.f;
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output->assign(zerof); // fill up output tensor with zeros
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auto matrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT<T>(), context);
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auto compound = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT<T>(), context);
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auto permutation = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT<T>(), context);
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auto lowerMatrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT<T>(), context);
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auto upperMatrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT<T>(), context);
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float zero = 0.f;
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for (sd::LongType e = 0; e < totalCount; e++) {
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if (e) matrix->assign(zero);
|
|
|
|
for (sd::LongType k = e * n2, row = 0; k < (e + 1) * n2; k++) {
|
|
matrix->p(row++, input->e<T>(k));
|
|
}
|
|
T det = lup_<T, sd::LongType>(context, matrix, compound, permutation).template e<T>(0);
|
|
|
|
// FIXME: and how this is going to work on float16?
|
|
if (sd::math::sd_abs<T,T>(det) < T(0.000001)) {
|
|
sd_printf("matrix_inverse: The matrix %i has no inverse due determinant is %lf. Quiting...\n", e, det);
|
|
return sd::Status::VALIDATION;
|
|
}
|
|
lowerMatrix->setIdentity(); // set up U to identity matrix
|
|
for (sd::LongType k = 1; k < n; k++) { // and then put all values under main diagonal on to it
|
|
for (sd::LongType j = 0; j < k; j++) lowerMatrix->template r<T>(k, j) = compound->template t<T>(k, j);
|
|
}
|
|
upperMatrix->setIdentity(); // set up U to identity matrix
|
|
for (sd::LongType k = 0; k < n; k++) { // and then put all values under main diagonal on to it
|
|
for (sd::LongType j = k; j < n; j++) upperMatrix->template r<T>(k, j) = compound->template t<T>(k, j);
|
|
}
|
|
invertUpperMatrix(upperMatrix, matrix);
|
|
|
|
invertLowerMatrix(lowerMatrix, upperMatrix);
|
|
|
|
sd::MmulHelper::mmul(matrix, upperMatrix, compound, 1.0, 0.0);
|
|
sd::MmulHelper::mmul(compound, permutation, matrix, 1.0, 0.0);
|
|
for (sd::LongType k = e * n2, row = 0; k < (e + 1) * n2; k++) {
|
|
output->r<T>(k) = matrix->template t<T>(row++);
|
|
}
|
|
}
|
|
|
|
delete matrix;
|
|
delete compound;
|
|
delete upperMatrix;
|
|
delete lowerMatrix;
|
|
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
template <typename T>
|
|
static sd::Status lowerInverse_(LaunchContext* context, NDArray* input, NDArray* output) {
|
|
auto n = input->sizeAt(-1);
|
|
auto n2 = n * n;
|
|
auto totalCount = output->lengthOf() / n2;
|
|
float zero = 0.f;
|
|
output->assign(zero); // fill up output tensor with zeros
|
|
auto matrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT<T>(), context);
|
|
auto compound = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT<T>(), context);
|
|
auto permutation = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT<T>(), context);
|
|
auto lowerMatrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT<T>(), context);
|
|
auto upperMatrix = NDArrayFactory::create('c', {n, n}, DataTypeUtils::fromT<T>(), context);
|
|
for (sd::LongType e = 0; e < totalCount; e++) {
|
|
if (e) matrix->assign(zero);
|
|
|
|
for (sd::LongType k = e * n2, row = 0; k < (e + 1) * n2; k++) {
|
|
matrix->p(row++, input->e<T>(k));
|
|
}
|
|
T det = T(1.f);
|
|
for (auto i = 0; i < n; i++) {
|
|
det *= matrix->template t<T>(i, i);
|
|
}
|
|
|
|
// FIXME: a->d how this is going to work on float16?
|
|
if (sd::math::sd_abs<T,T>(det) < T(0.000001)) {
|
|
sd_printf("matrix_inverse: The matrix %i has no inverse due determinant is %lf. Quitting...\n", e, det);
|
|
return sd::Status::VALIDATION;
|
|
}
|
|
lowerMatrix->nullify();
|
|
invertLowerMatrix(matrix, lowerMatrix);
|
|
|
|
for (sd::LongType k = e * n2, row = 0; k < (e + 1) * n2; k++) {
|
|
output->r<T>(k) = lowerMatrix->template t<T>(row++);
|
|
}
|
|
}
|
|
|
|
delete matrix;
|
|
delete lowerMatrix;
|
|
delete compound;
|
|
delete permutation;
|
|
delete upperMatrix;
|
|
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
template <typename T>
|
|
static sd::Status upperInverse_(LaunchContext* context, NDArray* input, NDArray* output) {
|
|
auto n = input->sizeAt(-1);
|
|
auto n2 = n * n;
|
|
|
|
output->nullify(); // fill up output tensor with zeros
|
|
auto inputPart = input->allTensorsAlongDimension({-2, -1});
|
|
auto outputPart = output->allTensorsAlongDimension({-2, -1});
|
|
auto totalCount = outputPart.size();
|
|
for (sd::LongType e = 0; e < totalCount; e++) {
|
|
invertUpperMatrix(inputPart.at(e), outputPart.at(e));
|
|
}
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
sd::Status inverse(sd::LaunchContext* context, NDArray* input, NDArray* output) {
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), return inverse_, (context, input, output), SD_FLOAT_TYPES);
|
|
}
|
|
|
|
sd::Status lowerInverseFunctor(sd::LaunchContext* context, NDArray* input, NDArray* output) {
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), return lowerInverse_, (context, input, output), SD_FLOAT_TYPES);
|
|
}
|
|
|
|
sd::Status upperInverseFunctor(sd::LaunchContext* context, NDArray* input, NDArray* output) {
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), return upperInverse_, (context, input, output), SD_FLOAT_TYPES);
|
|
}
|
|
|
|
template <typename T>
|
|
static bool checkCholeskyInput_(sd::LaunchContext* context, NDArray * input) {
|
|
ResultSet lastMatrixList = input->allTensorsAlongDimension({input->rankOf() - 2, input->rankOf() - 1});
|
|
for (sd::LongType i = 0; i < lastMatrixList.size(); i++) {
|
|
auto thisMatrix = lastMatrixList.at(i);
|
|
// check for symmetric
|
|
for (sd::LongType r = 0; r < thisMatrix->rows(); r++)
|
|
for (sd::LongType c = 0; c < thisMatrix->columns(); c++)
|
|
if (sd::math::sd_abs<T,T>(thisMatrix->e<T>(r, c) - lastMatrixList.at(i)->e<T>(c, r)) >
|
|
DataTypeUtils::min_positive<T>())
|
|
return false;
|
|
|
|
NDArray *output = NDArrayFactory::create<T>(static_cast<T>(0.), context);
|
|
if (sd::Status::OK != determinant(context, thisMatrix, output)) return false;
|
|
if (output->e<T>(0) <= T(0)) return 0;
|
|
NDArray reversedMatrix(*thisMatrix);
|
|
if (sd::Status::OK != inverse(context, thisMatrix, &reversedMatrix)) return false;
|
|
if (sd::Status::OK != determinant(context, &reversedMatrix, output)) return false;
|
|
if (output->e<T>(0) <= T(0)) return 0;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
bool checkCholeskyInput(sd::LaunchContext* context, NDArray * input) {
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), return checkCholeskyInput_, (context, input), SD_FLOAT_TYPES);
|
|
}
|
|
|
|
template <typename T>
|
|
sd::Status cholesky_(LaunchContext* context, NDArray* input, NDArray* output, bool inplace) {
|
|
auto n = input->sizeAt(-1);
|
|
auto n2 = n * n;
|
|
auto totalCount = output->lengthOf() / n2;
|
|
float zero = 0.f;
|
|
if (!inplace) output->assign(zero); // fill up output tensor with zeros only inplace=false
|
|
|
|
std::vector<sd::LongType> shape = {n,n};
|
|
std::unique_ptr<NDArray> matrix(
|
|
NDArrayFactory::create_('c', shape, input->dataType(), context)); //, block.getWorkspace());
|
|
std::unique_ptr<NDArray> lowerMatrix(NDArrayFactory::create_('c',shape, input->dataType(), context));
|
|
|
|
for (sd::LongType e = 0; e < totalCount; e++) {
|
|
// fill up matrix
|
|
for (sd::LongType k = e * n2, l = 0; k < (e + 1) * n2; k++) {
|
|
matrix->p(l++, input->e<T>(k));
|
|
}
|
|
// if (e) // from the second loop need to zero matrix
|
|
lowerMatrix->assign(zero);
|
|
|
|
for (sd::LongType col = 0; col < n; col++) {
|
|
for (sd::LongType row = 0; row < col; row++) {
|
|
T rowSum = static_cast<T>(0);
|
|
for (sd::LongType k = 0; k < row; ++k) rowSum += (lowerMatrix->e<T>(col, k) * lowerMatrix->e<T>(row, k));
|
|
lowerMatrix->p(col, row, (matrix->e<T>(row, col) - rowSum) / lowerMatrix->e<T>(row, row));
|
|
}
|
|
T diagonalSum = static_cast<T>(0);
|
|
for (sd::LongType k = 0; k < col; ++k) diagonalSum += lowerMatrix->e<T>(col, k) * lowerMatrix->e<T>(col, k);
|
|
lowerMatrix->p(col, col, sd::math::sd_sqrt<T, T>(matrix->e<T>(col, col) - diagonalSum));
|
|
}
|
|
for (sd::LongType k = e * n2, l = 0; k < (e + 1) * n2; k++) {
|
|
output->p(k, lowerMatrix->e<T>(l++));
|
|
}
|
|
}
|
|
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
sd::Status cholesky(sd::LaunchContext* context, NDArray* input, NDArray* output, bool inplace) {
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), return cholesky_, (context, input, output, inplace), SD_FLOAT_TYPES);
|
|
}
|
|
|
|
template <typename T>
|
|
sd::Status logdetFunctor_(LaunchContext* context, NDArray* input, NDArray* output) {
|
|
auto tempOutput = input->dup();
|
|
auto res = cholesky_<T>(context, input, tempOutput, false);
|
|
if (res != sd::Status::OK) return res;
|
|
auto n = input->sizeAt(-1);
|
|
auto totalCount = output->lengthOf();
|
|
std::vector<T> d(n);
|
|
ResultSet matrices = tempOutput->allTensorsAlongDimension({input->rankOf() - 2, input->rankOf() - 1});
|
|
|
|
for (sd::LongType e = 0; e < totalCount; e++) {
|
|
for (sd::LongType i = 0; i < n; ++i)
|
|
output->r<T>(e) += sd::math::sd_log<T, T>(sd::math::sd_pow<T, T, T>(matrices.at(e)->t<T>(i, i), T(2)));
|
|
}
|
|
delete tempOutput; // Clean up duped array
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
sd::Status logdetFunctor(sd::LaunchContext* context, NDArray* input, NDArray* output) {
|
|
BUILD_SINGLE_SELECTOR(input->dataType(), return logdetFunctor_, (context, input, output), SD_FLOAT_TYPES);
|
|
}
|
|
|
|
sd::Status lup(sd::LaunchContext* context, NDArray* input, NDArray* compound, NDArray* permutation) {
|
|
BUILD_DOUBLE_SELECTOR(input->dataType(), permutation->dataType(), lup_, (context, input, compound, permutation),
|
|
SD_FLOAT_NATIVE, SD_INDEXING_TYPES);
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
} // namespace helpers
|
|
} // namespace ops
|
|
} // namespace sd
|
|
#endif |