raver119 320924278d
Legacy API changes (#441)
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* one more initial commit

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* next step

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* next step

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* Refactored buffer() and shapeInfo() methods usage with NDArray class.

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* Adopt Graph class methods to use const shapes.

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* Adopt choose op to use constant shapes.

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* Adopt where op shape method to use constant shapes.

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* Adopt lstsq op to use constant empty shapes.

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* Adopt matrix_diag_part op shape routine to use constant shapes.

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* Adopt determinant ops to use constant shapes.

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* Adopt mean_pairwssqerr_loss ops to use constant shapes.

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* Adopt ops shape methods.

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* Adopt shape methods for loss ops.

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* Adopt log_loss op shape method.

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* Adopt shape methods for ops.

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* Adopt dilation2d ops shape methods.

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* Adopted deconv2d ops shape methods.

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* Adopted dynamicRNN op shape method.

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* Adopted shape methods for ops.

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* Adopted shape methods for lstm layer ops.

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* few updates

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* first cuda tweak

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* Adopt constant shapes for sconv2d ops.

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* Adopt constant shapes for gru ops.

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* Adopt constant shapes with shape methods for segment ops and so on.

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* Adopted constant shapes with unsorted_segment_* ops.

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* Adopted constant shapes with gamma op shape method.

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* Adopted shape methods of reduce_stddev ops.

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* Adopted shape methods for reduce_* ops.

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* Adopt shape method for squeeze op.

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* Adopt strided_slice shape method.

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* Refactored concat op shape method to adopt constant shapes.

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* Adopted shape method for mirror_pad op.

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* Adopted split op shape method.

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* Adopted tile ops shape methods.

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* Added const cast for mkldnn routines handles.

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* Refactored logSoftMaxForVector_ routine to conform with proper data and shape pointer casts.

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* Cosmetic changes to proper usage of constant pointers.

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* Refactored a couple shape comparators for strides and addBias helpers to proper use data pointers with inplace option.

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* Refactored depthToSpace helpers.

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* Refactored histogram helpers.

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* Refactored im2col helpers.

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* Refactored gather and gatherND helpers.

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* Fixed buffer usage on percentile helper.

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* Fixed gather shape with helpers and range buffer usage.

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* Fixed buffer usage with space to depth helpers.

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* Fixed buffer usage and constant shapes.

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* Fixed buffer usage with LUP decomposition>

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* Refactored onehot_ helper.

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* Refactored pad and prefix to use constant shapes.

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* Refactoed softmax helpers.

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* Fixed space to batch helpers to use buffers properly.

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* Fixed stack and split helpers.

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* Fixed buffer usage with sparse to dense helpers.

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* Fixed buffer usage with mindistance_ helpers.

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* Fixed buffer usage with tile helper.

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* Fixed constant shape usage.

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* Fixed constant shape usage with legacy pairwise bool ops.

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* Refactored a couple of methods to adopt constant shape usage.

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* Fixed broadcasting with constant shape."

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* Fixed const usage with inplace reverse and constant shapes with legacy reduction.

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* Refactored legacy ops with const shapes.

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* Refactored sort to adopt constant shapes.

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* Corrected sort for constant shape usage.

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* Fixed constant shape usage with special methods.

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* Refactored Context to conform with constant shape usage.

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* CUDA broadcasting headers

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* pairwise/indexreduce/random headers

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* Refactored native ops to adopt constant shapes.

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* legacy reduce3/scalar headers

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* Corrected pullRow signature and tests.

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* Corrected routines to proper use of constant shapes.

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* Refactored tests to use constant shapes properly.

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* Refactored legacy ops tests to use constant shapes properly.

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* Refactored buffer usage with NDArray tests.

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* Fixed native ops tests.

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* Fixed special concat routine.

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* Fixed buffer usage with test.

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* Fixed buffer usage with a test.

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* Refactored TAD.h and tests.

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* Refactored calcStrides* routines to use constant shapes.

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* Fixed miscelaneous errors with constant shapes.

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* NativeOps const changes

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* Corrected definitions for declared functions.

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* NativeOps const changes

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* few more const changes

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* Fixed const shapes with shape routines.

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* few more const changes

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* Fixed shape method for broadcastable case.

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* few more const changes

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* xw_plus_b BP shape fn restored

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* Fixed signatures with broadcasting.

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* Repaired backprops shape methods for a set of operations.

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* Refactored broadcast bool for cuda.

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* Refactored methods for 3 args with const qualifier.

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* Fixed a couple of kernel signatures for broadcasting.

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* Fixed kernels signatures for const buffers and shapes.

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* Refactored pairwise methods to persistent buffers and shapes usage.

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* Adopt const to buffers and shapes with kernels.

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* Adopt const to buffers and shapes with scalar kernels.

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* Refactored indexreduce kernels signatures to use const buffers and shapes.

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* Refactored pairwise kernels to adopt cons shapes and buffers.

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* Refactored pairwise bool kernels to adopt cons shapes and buffers.

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* Refactored random special ops to conform with const shapes and buffers.

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* Refactored native ops to conform with const shapes and buffers under cuda platform.

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* Cosmetical changes only.

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* Fixed const shapes and buffers error.

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* Corrected start pos routine.

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* Refactored methods to conform with const shapes and buffers.

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* Refactored helpers to use proper methods instead.

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* bunch of changes

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* next bunch of changes

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* next bunch of changes

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* Fixed execScalar declaration.

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* Fixed execScalar declaration.

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* Corrected const shape cases with sort and so on.

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* Fixed const shapes for sort.

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* Refactored kernel declarations to adopt const shapes.

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* Fixed kernels declarations to adopt const shapes.

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* Corrected kernel declarations to adopt const shapes and buffers.

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* Fixed kernels declarations to adopt const shapes.

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* Fixed segment helpers kernels declarations and so on to adopt const shapes.

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* Fixed const shape usage with segment and solve helpers.

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* Fixed kernel declaration with adjustWeight helper.

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* Fixed cuda implementations for constant shape helpers.

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* Adopted const shape usage with kernels.

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* Adopted top_k kernels to use const shapes and buffers.

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* Corrected kernels declarations to adopt const shapes with helpers.

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* Refactored NDArray definitions to adopt const shapes and buffers.

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* Fixed const shapes with image suppression helpers.

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* Slight improvement with buffers.

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* Refactored buffer usage.

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* Refactored buffer usage with tests.

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* Fixed const shape usage with definitions.

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* minor updates on cpu side

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* Refactored const shape usage with ConstantDescritor and native ops with cuda platform.

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* Refactored tear and tile kernels to adopt with const shapes.

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* softmax_loop fix

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* update missing signature

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* softmax again

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* few more missing consts

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* new methods updated

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Co-authored-by: shugeo <sgazeos@gmail.com>
2020-05-09 08:06:14 +03:00

136 lines
6.1 KiB
C++

/*******************************************************************************
* 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 <sgazeos@gmail.com> at 01/28/2020
//
#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_lstsq)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/lstsq.h>
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