120 lines
5.6 KiB
Plaintext
120 lines
5.6 KiB
Plaintext
/*
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* ******************************************************************************
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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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//
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// @author GS <sgazeos@gmail.com>
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//
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#include <system/op_boilerplate.h>
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#include <array/NDArray.h>
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#include <helpers/MmulHelper.h>
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#include <helpers/ShapeUtils.h>
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#include <helpers/ConstantTadHelper.h>
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#include <ops/declarable/helpers/triangular_solve.h>
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#include <ops/declarable/helpers/lup.h>
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#include <ops/declarable/helpers/qr.h>
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#include <ops/declarable/helpers/lstsq.h>
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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 __global__ void fillRegularizerKernel(T* ioMatrixData, const Nd4jLong* ioMatrixShape, const Nd4jLong* ioMatrixTads, const Nd4jLong* ioMatrixOffsets, Nd4jLong batchSize, Nd4jLong rows, T const value) {
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for (auto x = blockIdx.x; x < batchSize; x += gridDim.x) {
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auto z = ioMatrixData + ioMatrixOffsets[x];
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for (auto r = threadIdx.x; r < rows; r += blockDim.x) {
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Nd4jLong pos[] = {r,r};
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auto zIndex = shape::getOffset(ioMatrixTads, pos);
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z[zIndex] = value;
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}
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}
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}
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template <typename T>
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static void fillRegularizer(sd::LaunchContext* context, NDArray& ioMatrix, double const value) {
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auto lastDimsTads = ConstantTadHelper::getInstance().tadForDimensions(ioMatrix.shapeInfo(), {-2, -1});
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auto stream = context->getCudaStream();
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auto rows = ioMatrix.sizeAt(-2);
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//auto cols = ioMatrix.sizeAt(-1);
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fillRegularizerKernel<T><<<256, 256, 128, *stream>>>(ioMatrix.dataBuffer()->specialAsT<T>(), ioMatrix.specialShapeInfo(), lastDimsTads.specialShapeInfo(), lastDimsTads.specialOffsets(), lastDimsTads.numberOfTads(), rows, (T)value);
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}
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template <typename T>
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int leastSquaresSolveFunctor_(sd::LaunchContext* context, NDArray const* leftInput, NDArray const* rightInput, double const l2Regularizer, bool const fast, NDArray* output) {
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if (fast) { // Cholesky decomposition approach
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// Equation for solve A^T * Ax = A^T * b, so
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// 1. Computing A2:
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auto tAtShape = ShapeUtils::evalShapeForMatmul(leftInput->shapeInfo(), leftInput->shapeInfo(), true, false);
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//tAtShape[tAtShape.size() - 2] = output->sizeAt(-2);
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NDArray leftOutput(leftInput->ordering(), tAtShape, output->dataType(), context);
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MmulHelper::matmul(leftInput, leftInput, &leftOutput, true, false); // Computing A2 = A^T * A
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// 2. Computing B' = A^T * b
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auto rightOutput = output->ulike();
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MmulHelper::matmul(leftInput, rightInput, &rightOutput, true, false); // Computing B' = A^T * b
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// 3. Regularization ( indeed A' = A2 - l2Regularizer * I)
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if (l2Regularizer != 0.0) {
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auto regularizer = leftOutput.ulike(); regularizer.nullify();
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fillRegularizer<T>(context, regularizer, (T)l2Regularizer);
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leftOutput += regularizer;
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}
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// 4. Cholesky decomposition -- output matrix is square and lower triangular
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helpers::cholesky(context, &leftOutput, &leftOutput, true); // inplace decomposition
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// 5. Solve two triangular systems:
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auto rightB = rightOutput.ulike(); rightB.nullify();
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helpers::triangularSolveFunctor(context, &leftOutput, &rightOutput, true, false, &rightB);
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helpers::adjointMatrix(context, &leftOutput, true, &leftOutput);
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helpers::triangularSolveFunctor(context, &leftOutput, &rightB, false, false, output);
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// All done
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}
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else { // QR decomposition approach
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// Equation for solve Rx = Q^T * b, where A = Q * R, where Q - orthogonal matrix, and R - upper triangular
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// 1. QR decomposition
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auto qShape = leftInput->getShapeAsVector();
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auto rShape = leftInput->getShapeAsVector();
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qShape[leftInput->rankOf() - 1] = leftInput->sizeAt(-2);
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NDArray Q(leftInput->ordering(), qShape, leftInput->dataType(), context);// = leftInput->ulike();
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NDArray R(leftInput->ordering(), rShape, leftInput->dataType(), context); // = rightInput->ulike();
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helpers::qr(context, leftInput, &Q, &R, true);
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// 2. b` = Q^t * b:
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auto rightOutput = rightInput->ulike();
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MmulHelper::matmul(&Q, rightInput, &rightOutput, true, false);
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// 3. Solve triangular system
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helpers::triangularSolveFunctor(context, &R, &rightOutput, false, false, output);
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}
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return Status::OK();
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}
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int leastSquaresSolveFunctor(sd::LaunchContext* context, NDArray const* leftInput, NDArray const* rightInput, double const l2Regularizer, bool const fast, NDArray* output) {
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BUILD_SINGLE_SELECTOR(leftInput->dataType(), return leastSquaresSolveFunctor_, (context, leftInput, rightInput, l2Regularizer, fast, output), FLOAT_TYPES);
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}
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}
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}
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}
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