raver119 320924278d
Legacy API changes (#441)
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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

444 lines
15 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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
******************************************************************************/
//
// @author raver119@gmail.com
//
#ifndef LIBND4J_TADTESTS_H
#define LIBND4J_TADTESTS_H
#include "testlayers.h"
#include <array/NDArray.h>
#include <helpers/TAD.h>
#include <array>
#include <helpers/ConstantTadHelper.h>
using namespace sd;
class TadTests : public testing::Test {
public:
int numLoops = 100000000;
int extLoops = 1000;
int intLoops = 1000;
};
TEST_F(TadTests, Test4DTad1) {
NDArray* arraySource = sd::NDArrayFactory::linspace(1.0f, 10000.0f, 10000);
Nd4jLong badShape[] = {4, 2, 1, 4, 4, 80, 16, 4, 1, 8192, -1, 99};
Nd4jLong goodShape[] = {4, 2, 1, 4, 4, 16, 16, 4, 1, 8192, 1, 99};
std::vector<float> buff = arraySource->getBufferAsVector<float>();
NDArray* arrayExp = new NDArray(buff.data(), goodShape);
NDArray* arrayBad = new NDArray(buff.data(), badShape);
int dim = 1;
shape::TAD tad;
tad.init(arrayBad->shapeInfo(), &dim, 1);
tad.createTadOnlyShapeInfo();
tad.createOffsets();
int exp[] = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95 };
for (int e = 0; e < 32; e++)
ASSERT_EQ((int) tad.tadOffsets[e], exp[e]);
delete arrayExp;
delete arrayBad;
delete arraySource;
}
TEST_F(TadTests, TestNumTads1) {
auto x = NDArrayFactory::create<float>('c', {2, 3});
auto y = NDArrayFactory::create<float>('c', {2, 2});
std::vector<int> dim({0});
Nd4jLong tadLengthX = shape::tadLength(x.shapeInfo(), dim.data(), dim.size());
Nd4jLong numTadsX = x.lengthOf() / tadLengthX;
Nd4jLong tadLengthY = shape::tadLength(y.shapeInfo(), dim.data(), dim.size());
Nd4jLong numTadsY = y.lengthOf() / tadLengthY;
ASSERT_EQ(2, tadLengthX);
ASSERT_EQ(3, numTadsX);
ASSERT_EQ(2, tadLengthY);
ASSERT_EQ(2, numTadsY);
}
TEST_F(TadTests, TestShapeTad_1) {
float buff[] = {1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,16,17,18,19,20,21,22,23,24};
Nd4jLong shapeInfo[] = {3, 2, 3, 4, 12, 4, 1, 8192, 1, 99};
NDArray input(buff, shapeInfo);
std::vector<int> dimensions = {0,1,2};
Nd4jLong tadLength = shape::tadLength(input.shapeInfo(), dimensions.data(), dimensions.size());
Nd4jLong numTads = input.lengthOf() / tadLength;
shape::TAD tad;
tad.init(input.shapeInfo(), dimensions.data(), dimensions.size());
tad.createTadOnlyShapeInfo();
tad.createOffsets();
auto tadShapeInfo = new Nd4jLong[shape::shapeInfoLength(tad.tadOnlyShapeInfo[0])];
std::memcpy(tadShapeInfo, tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo));
float* tadBuff = reinterpret_cast<float*>(input.buffer()) + tad.tadOffsets[0];
NDArray tadArr(tadBuff, tadShapeInfo);
ASSERT_TRUE(numTads==1);
ASSERT_TRUE(input.isSameShapeStrict(tadArr));
ASSERT_TRUE(input.equalsTo(&tadArr));
delete[] tadShapeInfo;
}
TEST_F(TadTests, TadNoAxis_1) {
auto array = NDArrayFactory::create<float>('c', {2, 3});
shape::TAD tad;
tad.init(array.shapeInfo(), nullptr, 0);
tad.createTadOnlyShapeInfo();
tad.createOffsets();
ASSERT_TRUE(tad.wholeThing);
ASSERT_TRUE(shape::equalsStrict(tad.tadOnlyShapeInfo, array.shapeInfo()));
}
TEST_F(TadTests, TadEdgeCase_1) {
auto array = NDArrayFactory::create<float>('c', {5, 4, 1});
auto exp = NDArrayFactory::create<float>('c', {5, 4});
array.linspace(1);
auto tad = array(0, {2});
ASSERT_TRUE(exp.isSameShape(tad));
}
TEST_F(TadTests, TestEdgeCase_2) {
auto array = NDArrayFactory::create<float>('f', {2, 3, 1}, {1, 4, 2, 5, 3, 6});
for (int e = 0 ; e < array.lengthOf(); e++) {
auto tad = array(e, {0,1});
ASSERT_NEAR(tad.e<float>(0), array.e<float>(e), 1e-5);
}
}
TEST_F(TadTests, TadEdgeCase_2) {
auto array = NDArrayFactory::create<float>('c', {2, 3, 4});
auto tad = array(0, {0,2});
ASSERT_EQ(3, tad.lengthOf());
}
TEST_F(TadTests, test_Tad_Ews_optimization_1) {
shape::TAD xTad;
std::array<int,2> array = {1,2};
ASSERT_TRUE(xTad.dimensionsDescending(3, array.data(), array.size()));
}
TEST_F(TadTests, test_Tad_Ews_optimization_2) {
shape::TAD xTad;
std::array<int,2> array = {0,2};
ASSERT_FALSE(xTad.dimensionsDescending(3, array.data(), array.size()));
}
TEST_F(TadTests, test_Tad_Ews_optimization_3) {
shape::TAD xTad;
std::array<int,1> array = {1};
ASSERT_TRUE(xTad.dimensionsDescending(2, array.data(), array.size()));
}
TEST_F(TadTests, test_Tad_Ews_optimization_4) {
shape::TAD xTad;
std::array<int,1> array = {0};
ASSERT_TRUE(xTad.dimensionsDescending(1, array.data(), array.size()));
}
TEST_F(TadTests, test_Tad_Ews_optimization_5) {
shape::TAD xTad;
std::array<int,2> array = {2,3};
ASSERT_TRUE(xTad.dimensionsDescending(4, array.data(), array.size()));
}
TEST_F(TadTests, test_TAD_empty_dims_1) {
Nd4jLong xShape[8] = {2, 150, 1, 3, 1, 16384, 3, 99};
shape::TAD xTad;
xTad.init(xShape, reinterpret_cast<int*>(112L), 0);
xTad.createTadOnlyShapeInfo();
xTad.createOffsets();
}
TEST_F(TadTests, test_tad_order_1) {
Nd4jLong xShape[8] = {2, 150, 10, 10, 1, 8192, 1, 99};
Nd4jLong tShape[8] = {2, 1, 10, 1, 1, 8192, 1, 99};
shape::TAD xTad;
int dim = 1;
xTad.init(xShape, &dim, 1);
xTad.createTadOnlyShapeInfo();
ASSERT_TRUE(shape::equalsStrict(tShape, xTad.tadOnlyShapeInfo));
}
TEST_F(TadTests, test_tad_order_2) {
Nd4jLong xShape[8] = {2, 150, 10, 10, 1, 8192, 1, 99};
Nd4jLong tShape[8] = {2, 1, 150, 1, 10, 8192, 10, 99};
shape::TAD xTad;
int dim = 0;
xTad.init(xShape, &dim, 1);
xTad.createTadOnlyShapeInfo();
ASSERT_TRUE(shape::equalsStrict(tShape, xTad.tadOnlyShapeInfo));
}
TEST_F(TadTests, test_tad_order_3) {
Nd4jLong xShape[10] = {3, 10, 20, 30, 600 ,30, 1, 8192, 1, 99};
Nd4jLong tShape[8] = {2, 1, 30, 1, 1, 8192, 1, 99};
shape::TAD xTad;
int dim = 2;
xTad.init(xShape, &dim, 1);
xTad.createTadOnlyShapeInfo();
ASSERT_TRUE(shape::equalsStrict(tShape, xTad.tadOnlyShapeInfo));
}
TEST_F(TadTests, test_tad_order_4) {
Nd4jLong xShape[10] = {3, 10, 20, 30, 600 ,30, 1, 8192, 1, 99};
Nd4jLong tShape[8] = {2, 20, 30, 30, 1, 8192, 1, 99};
shape::TAD xTad;
int dim[2] = {1, 2};
xTad.init(xShape, dim, 2);
xTad.createTadOnlyShapeInfo();
ASSERT_TRUE(shape::equalsStrict(tShape, xTad.tadOnlyShapeInfo));
}
TEST_F(TadTests, test_column_1) {
auto x = NDArrayFactory::create<float>('c', {5, 2});
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), 0);
ASSERT_EQ(1, shape::rank(tadPack.primaryShapeInfo()));
ASSERT_EQ(5, shape::length(tadPack.primaryShapeInfo()));
ASSERT_TRUE(shape::isVector(tadPack.primaryShapeInfo()));
auto scalarViewPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(tadPack.primaryShapeInfo(), 0);
ASSERT_TRUE(shape::equalsStrict(tadPack.primaryShapeInfo(), scalarViewPack.primaryShapeInfo()));
}
///////////////////////////////////////////////////////////////////
TEST_F(TadTests, calcOffsets_1) {
Nd4jLong shapeInfoF[10] = {3, 2,3,4, 1,2,6, 8192, 1, 102};
Nd4jLong shapeInfoC[10] = {3, 2,3,4, 12,4,1, 8192, 1, 99};
Nd4jLong shapeInfoFC[10] = {3, 2,3,4, 1,2,6, 8192, 1, 99};;
Nd4jLong expOffsetsF[24] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23};
Nd4jLong expOffsetsC[24] = {0,12,4,16,8,20,1,13,5,17,9,21,2,14,6,18,10,22,3,15,7,19,11,23};
Nd4jLong offsets[24];
shape::calcOffsets(shapeInfoF, offsets, 'f');
for (int e = 0; e < 24; e++)
ASSERT_TRUE(offsets[e] == expOffsetsF[e]);
shape::calcOffsets(shapeInfoC, offsets, 'f');
for (int e = 0; e < 24; e++)
ASSERT_TRUE(offsets[e] == expOffsetsC[e]);
shape::calcOffsets(shapeInfoFC, offsets, 'f');
for (int e = 0; e < 24; e++)
ASSERT_TRUE(offsets[e] == expOffsetsF[e]);
}
/////////////////////////////////////////////////////////////////
TEST_F(TadTests, outerArrayIndexes_1) {
NDArray x('c', {2,3,4,5}, sd::DataType::FLOAT32);
int maxIdxs[120];
NDArray y1('c', {3,5}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude1 = {0,2};
const int n1[] = {20,25,30,35, 80,85,90,95};
int minIdx = 5;
int N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y1.shapeInfo(), dimsToExclude1.data());
ASSERT_TRUE(N == x.lengthOf()/y1.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n1[i] == maxIdxs[i]);
NDArray y2('c', {4,5}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude2 = {0,1};
const int n2[] = {12,32,52, 72,92,112};
minIdx = 12;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y2.shapeInfo(), dimsToExclude2.data());
ASSERT_TRUE(N == x.lengthOf()/y2.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n2[i] == maxIdxs[i]);
NDArray y3('c', {2,5}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude3 = {1,2};
const int n3[] = {64,69,74,79,84,89,94,99,104,109,114,119};
minIdx = 9;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y3.shapeInfo(), dimsToExclude3.data());
ASSERT_TRUE(N == x.lengthOf()/y3.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n3[i] == maxIdxs[i]);
NDArray y4('c', {2,3}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude4 = {2,3};
const int n4[] = {20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39};
minIdx = 1;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y4.shapeInfo(), dimsToExclude4.data());
ASSERT_TRUE(N == x.lengthOf()/y4.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n4[i] == maxIdxs[i]);
NDArray y5('c', {2,4}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude5 = {1,3};
const int n5[] = {65,66,67,68,69, 85,86,87,88,89, 105,106,107,108,109};
minIdx = 5;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y5.shapeInfo(), dimsToExclude5.data());
ASSERT_TRUE(N == x.lengthOf()/y5.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n5[i] == maxIdxs[i]);
NDArray y6('c', {2,3,4}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude6 = {3};
const int n6[] = {65,66,67,68,69};
minIdx = 13;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y6.shapeInfo(), dimsToExclude6.data());
ASSERT_TRUE(N == x.lengthOf()/y6.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n6[i] == maxIdxs[i]);
NDArray y7('c', {4}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude7 = {0,1,3};
const int n7[] = {15,16,17,18,19, 35,36,37,38,39, 55,56,57,58,59, 75,76,77,78,79, 95,96,97,98,99, 115,116,117,118,119};
minIdx = 3;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y7.shapeInfo(), dimsToExclude7.data());
ASSERT_TRUE(N == x.lengthOf()/y7.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n7[i] == maxIdxs[i]);
NDArray y8('c', {5}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude8 = {0,1,2};
const int n8[] = {0,5,10,15, 20,25,30,35, 40,45,50,55, 60,65,70,75, 80,85,90,95, 100,105,110,115};
minIdx = 0;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y8.shapeInfo(), dimsToExclude8.data());
ASSERT_TRUE(N == x.lengthOf()/y8.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n8[i] == maxIdxs[i]);
NDArray y9('c', {2}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude9 = {1,2,3};
const int n9[] = {60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119};
minIdx = 1;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y9.shapeInfo(), dimsToExclude9.data());
ASSERT_TRUE(N == x.lengthOf()/y9.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n9[i] == maxIdxs[i]);
NDArray y10('c', {3,4,5}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude10 = {0};
const int n10[] = {11, 71};
minIdx = 11;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y10.shapeInfo(), dimsToExclude10.data());
ASSERT_TRUE(N == x.lengthOf()/y10.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n10[i] == maxIdxs[i]);
NDArray y11('c', {2,4,5}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude11 = {1};
const int n11[] = {66, 86, 106};
minIdx = 26;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y11.shapeInfo(), dimsToExclude11.data());
ASSERT_TRUE(N == x.lengthOf()/y11.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n11[i] == maxIdxs[i]);
NDArray y12('c', {3,2}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude12 = {0,2};
const int n12[] = {0,2,4,5,7,9,10,12,14,15,17,19,60,62,64,65,67,69,70,72,74,75,77,79};
minIdx = 0;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y12.shapeInfo(), dimsToExclude12.data());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n12[i] == maxIdxs[i]);
NDArray y13('c', {3,2}, sd::DataType::FLOAT32);
const std::vector<int> dimsToExclude13 = {0,2};
const int n13[] = {1,3,6,8,11,13,16,18,61,63,66,68,71,73,76,78};
minIdx = 1;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y13.shapeInfo(), dimsToExclude13.data());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n13[i] == maxIdxs[i]);
NDArray y14('c', {4,5}, sd::DataType::FLOAT32);
const int n14[] = {12,32,52, 72,92,112};
minIdx = 12;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y14.shapeInfo(), nullptr);
ASSERT_TRUE(N == x.lengthOf()/y14.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n14[i] == maxIdxs[i]);
NDArray y15('c', {3,4,5}, sd::DataType::FLOAT32);
const int n15[] = {11, 71};
minIdx = 11;
N = shape::outerArrayIndexes(maxIdxs, minIdx, x.shapeInfo(), y15.shapeInfo(), nullptr);
ASSERT_TRUE(N == x.lengthOf()/y15.lengthOf());
for(int i = 0; i < N; ++i)
ASSERT_TRUE(n15[i] == maxIdxs[i]);
}
#endif //LIBND4J_TADTESTS_H