wehub-resource-sync
153 lines
6.0 KiB
C++
153 lines
6.0 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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// Created by GS <sgazeos@gmail.com> at 2/26/2018
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//
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/lup.h>
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_matrix_determinant)
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(matrix_determinant, 1, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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REQUIRE_TRUE(input->rankOf() >= 2, 0,
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"matrix_determinant: The rank of input array should not less than 2, but %i is given", input->rankOf());
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REQUIRE_TRUE(input->sizeAt(-1) == input->sizeAt(-2), 0,
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"matrix_determinant: The last two dimensions should be equal, but %i and %i are given",
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input->sizeAt(-1), input->sizeAt(-2));
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return helpers::determinant(block.launchContext(), input, output);
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}
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DECLARE_SHAPE_FN(matrix_determinant) {
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auto inShape = inputShape->at(0);
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LongType * determinantShape;
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int targetRank = shape::rank(inShape) - 2; // last two dimensions will be reduced to scalar
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if (targetRank == 0) { // scalar only
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determinantShape = ConstantShapeHelper::getInstance().scalarShapeInfo(ArrayOptions::dataType(inShape));
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} else if (targetRank == 1) { // vector
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determinantShape =
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ConstantShapeHelper::getInstance().vectorShapeInfo(shape::sizeAt(inShape, static_cast<LongType>(0)), ArrayOptions::dataType(inShape));
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} else { // only two last dimensions are excluded
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determinantShape = ConstantShapeHelper::getInstance().createShapeInfo(
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ArrayOptions::dataType(inShape), shape::order(inShape), targetRank, shape::shapeOf(inShape), -1);
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}
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return SHAPELIST(determinantShape);
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}
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DECLARE_TYPES(matrix_determinant) {
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getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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} // namespace ops
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} // namespace sd
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#endif
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#if NOT_EXCLUDED(OP_log_matrix_determinant)
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namespace sd {
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namespace ops {
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DECLARE_TYPES(log_matrix_determinant) {
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getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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CUSTOM_OP_IMPL(log_matrix_determinant, 1, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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REQUIRE_TRUE(input->rankOf() >= 2, 0,
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"log_matrix_determinant: The rank of input array should not less than 2, but %i is given",
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input->rankOf());
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REQUIRE_TRUE(input->sizeAt(-1) == input->sizeAt(-2), 0,
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"log_matrix_determinant: The last two dimensions should be equal, but %i and %i are given",
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input->sizeAt(-1), input->sizeAt(-2));
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return helpers::logAbsDeterminant(block.launchContext(), input, output);
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}
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DECLARE_SHAPE_FN(log_matrix_determinant) {
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auto inShape = inputShape->at(0);
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LongType * determinantShape;
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int targetRank = shape::rank(inShape) - 2; // last two dimensions will be reduced to scalar
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if (targetRank == 0) { // scalar only
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determinantShape = ConstantShapeHelper::getInstance().scalarShapeInfo(ArrayOptions::dataType(inShape));
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} else if (targetRank == 1) { // vector
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determinantShape =
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ConstantShapeHelper::getInstance().vectorShapeInfo(shape::sizeAt(inShape, static_cast<LongType>(0)), ArrayOptions::dataType(inShape));
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} else { // only two last dimensions are excluded
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determinantShape = ConstantShapeHelper::getInstance().createShapeInfo(
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ArrayOptions::dataType(inShape), shape::order(inShape), targetRank, shape::shapeOf(inShape), -1);
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}
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return SHAPELIST(determinantShape);
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}
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} // namespace ops
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} // namespace sd
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#endif
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#if NOT_EXCLUDED(OP_logdet)
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namespace sd {
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namespace ops {
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DECLARE_TYPES(logdet) {
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getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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CUSTOM_OP_IMPL(logdet, 1, 1, false, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_NULLIFIED(0);
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REQUIRE_TRUE(input->rankOf() >= 2, 0, "logdet: The rank of input array should not less than 2, but %i is given",
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input->rankOf());
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REQUIRE_TRUE(input->sizeAt(-1) == input->sizeAt(-2), 0,
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"logdet: The last two dimensions should be equal, but %i and %i are given", input->sizeAt(-1),
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input->sizeAt(-2));
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REQUIRE_TRUE(helpers::checkCholeskyInput(block.launchContext(), input), 0,
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"logdet: The input tensor should be positive-defined hermitian.");
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return helpers::logdetFunctor(block.launchContext(), input, output);
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}
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DECLARE_SHAPE_FN(logdet) {
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auto inShape = inputShape->at(0);
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LongType* determinantShape;
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int targetRank = shape::rank(inShape) - 2; // last two dimensions will be reduced to scalar
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if (targetRank == 0) { // scalar only
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determinantShape = ConstantShapeHelper::getInstance().scalarShapeInfo(ArrayOptions::dataType(inShape));
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} else if (targetRank == 1) { // vector
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determinantShape =
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ConstantShapeHelper::getInstance().vectorShapeInfo(shape::sizeAt(inShape, static_cast<LongType>(0)), ArrayOptions::dataType(inShape));
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} else { // only two last dimensions are excluded
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determinantShape = ConstantShapeHelper::getInstance().createShapeInfo(
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ArrayOptions::dataType(inShape), shape::order(inShape), targetRank, shape::shapeOf(inShape), -1);
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}
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return SHAPELIST(determinantShape);
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}
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} // namespace ops
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} // namespace sd
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#endif
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