/******************************************************************************* * Copyright (c) 2020 Konduit, K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://www.apache.org/licenses/LICENSE-2.0. * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the * License for the specific language governing permissions and limitations * under the License. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // Created by GS at 01/28/2020 // #include #if NOT_EXCLUDED(OP_lstsq) #include #include namespace sd { namespace ops { CUSTOM_OP_IMPL(lstsq, 2, 1, false, 0, 0) { auto a = INPUT_VARIABLE(0); auto b = INPUT_VARIABLE(1); auto z = OUTPUT_NULLIFIED(0); bool fastFlag = true; double l2_factor = 0.; if (block.numB() > 0) { fastFlag = B_ARG(0); } if (block.numT() > 0) { l2_factor = T_ARG(0); } REQUIRE_TRUE(a->rankOf() >=2, 0, "lstsq: The rank of input left tensor should not be less than 2, but %i is given", a->rankOf()); REQUIRE_TRUE(b->rankOf() >=2, 0, "lstsq: The rank of input right tensor should not be less than 2, but %i is given", b->rankOf()); // REQUIRE_TRUE(a->sizeAt(-1) == a->sizeAt(-2), 0, "lstsq: The last two dimmensions should be equal, but %i and %i are given", a->sizeAt(-1), a->sizeAt(-2)); REQUIRE_TRUE(a->sizeAt(-2) == b->sizeAt(-2), 0, "lstsq: The last dimmension of left part should be equal to prelast of right part, but %i and %i are given", a->sizeAt(-1), b->sizeAt(-2)); //REQUIRE_TRUE(l2_factor == 0., 0, "lstsq: Implementation of operation is not finished for factor difference from 0."); if (a->isEmpty() || b->isEmpty() || z->isEmpty()) return Status::OK(); auto res = helpers::leastSquaresSolveFunctor(block.launchContext(), a, b, l2_factor, fastFlag, z); return res; } CUSTOM_OP_IMPL(solve_ls, 2, 1, false, 0, 0) { auto a = INPUT_VARIABLE(0); auto b = INPUT_VARIABLE(1); auto z = OUTPUT_NULLIFIED(0); bool fastFlag = true; double l2_factor = 0.; if (block.numB() > 0) { fastFlag = B_ARG(0); } if (block.numT() > 0) { l2_factor = T_ARG(0); } REQUIRE_TRUE(a->rankOf() >=2, 0, "lstsq: The rank of input left tensor should not be less than 2, but %i is given", a->rankOf()); REQUIRE_TRUE(b->rankOf() >=2, 0, "lstsq: The rank of input right tensor should not be less than 2, but %i is given", b->rankOf()); // REQUIRE_TRUE(a->sizeAt(-1) == a->sizeAt(-2), 0, "lstsq: The last two dimmensions should be equal, but %i and %i are given", a->sizeAt(-1), a->sizeAt(-2)); REQUIRE_TRUE(a->sizeAt(-2) == b->sizeAt(-2), 0, "lstsq: The last dimmension of left part should be equal to prelast of right part, but %i and %i are given", a->sizeAt(-1), b->sizeAt(-2)); //REQUIRE_TRUE(l2_factor == 0., 0, "lstsq: Implementation of operation is not finished for factor difference from 0."); auto res = Status::OK(); if (a->isEmpty() || b->isEmpty() || z->isEmpty()) return res; res = helpers::leastSquaresSolveFunctor(block.launchContext(), a, b, l2_factor, fastFlag, z); return res; } DECLARE_SYN(MatrixSolveLs, lstsq); DECLARE_SHAPE_FN(lstsq) { auto in0 = inputShape->at(0); auto in1 = inputShape->at(1); auto shapeOf = ShapeUtils::shapeAsVector(in1); auto rank = shapeOf.size(); shapeOf[rank - 2] = shape::sizeAt(in0, -1); if (shape::isEmpty(in0) || shape::isEmpty(in1)) { shapeOf[rank - 1] = 0; // set output shape to empty } auto resShape = ConstantShapeHelper::getInstance().createShapeInfo(ArrayOptions::dataType(in0), shape::order(in1), shapeOf);//ShapeBuilders::copyShapeInfoAndType(in1, in0, true, block.workspace()); if (shapeOf[rank - 1] == 0) { // ArrayOptions::setPropertyBit(resShape, ARRAY_EMPTY); resShape = ConstantShapeHelper::getInstance().emptyShapeInfo(ArrayOptions::dataType(in0)); } return SHAPELIST(resShape); } DECLARE_TYPES(lstsq) { getOpDescriptor() ->setAllowedInputTypes({ALL_FLOATS}) ->setAllowedOutputTypes({ALL_FLOATS}) ->setSameMode(false); } DECLARE_SHAPE_FN(solve_ls) { auto in0 = inputShape->at(0); auto in1 = inputShape->at(1); auto shapeOf = ShapeUtils::shapeAsVector(in1); auto rank = shapeOf.size(); shapeOf[rank - 2] = shape::sizeAt(in0, -1); if (shape::isEmpty(in0) || shape::isEmpty(in1)) { shapeOf[rank - 1] = 0; // set output shape to empty } auto resShape = ConstantShapeHelper::getInstance().createShapeInfo(ArrayOptions::dataType(in0), shape::order(in1), shapeOf);//ShapeBuilders::copyShapeInfoAndType(in1, in0, true, block.workspace()); if (shapeOf[rank - 1] == 0) { resShape = ConstantShapeHelper::getInstance().emptyShapeInfo(ArrayOptions::dataType(in1)); // ArrayOptions::setPropertyBit(resShape, ARRAY_EMPTY); } return SHAPELIST(resShape); } DECLARE_TYPES(solve_ls) { getOpDescriptor() ->setAllowedInputTypes({ALL_FLOATS}) ->setAllowedOutputTypes({ALL_FLOATS}) ->setSameMode(false); } } } #endif