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cavis/libnd4j/tests_cpu/layers_tests/LegacyOpsTests.cpp

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Eclipse Migration Initial Commit
2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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
******************************************************************************/
//
// Created by raver119 on 16.10.2017.
//
#include "testlayers.h"
#include <NDArray.h>
#include <ShapeUtils.h>
#include <reduce3.h>
#include <ops/declarable/LegacyTransformOp.h>
#include <ops/declarable/LegacyPairwiseTransformOp.h>
#include <ops/declarable/LegacyScalarOp.h>
#include <ops/declarable/LegacyReduceSameOp.h>
#include <ops/declarable/LegacyReduceFloatOp.h>
#include <ops/declarable/LegacyIndexReduceOp.h>
#include <ops/declarable/LegacyBroadcastOp.h>
#include <helpers/TAD.h>
#include <helpers/ConstantTadHelper.h>
using namespace nd4j;
using namespace nd4j::ops;
class LegacyOpsTests : public testing::Test {
};
TEST_F(LegacyOpsTests, TransformTests_1) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(1.0);
auto z = NDArrayFactory::create<float>('c', {5,5});
auto exp = NDArrayFactory::create<float>('c', {5, 5});
exp.assign(-1.0);
nd4j::ops::LegacyTransformSameOp op(transform::Neg); // Neg
auto status = op.execute({&x}, {&z}, {}, {}, {});
ASSERT_EQ(status, ND4J_STATUS_OK);
//z.printIndexedBuffer("Output NEG");
ASSERT_TRUE(z.equalsTo(&exp));
}
TEST_F(LegacyOpsTests, TransformTests_2) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(1.0);
auto exp = NDArrayFactory::create<float>('c', {5, 5});
exp.assign(-1.0);
nd4j::ops::LegacyTransformSameOp op(transform::Neg); // Neg
auto result = op.execute({&x}, {}, {});
ASSERT_EQ(1, result->size());
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(LegacyOpsTests, Reciprocal_1) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(2.0f);
auto ethalon = NDArrayFactory::create<float>('c', {5, 5});
ethalon.assign(0.5f);
nd4j::ops::LegacyTransformSameOp op(transform::Reciprocal); // Reciprocal
Nd4jStatus status = op.execute({&x}, {&x}, {}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, status);
ASSERT_TRUE(ethalon.equalsTo(&x));
}
TEST_F(LegacyOpsTests, PWT_Tests_1) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(2.0);
auto y = NDArrayFactory::create<float>('c', {5, 5});
y.assign(3.0);
auto exp = NDArrayFactory::create<float>('c', {5, 5});
exp.assign(6.0);
nd4j::ops::LegacyPairwiseTransformOp op(pairwise::Multiply); // Multiply
Nd4jStatus status = op.execute({&x, &y}, {&x}, {}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, status);
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(LegacyOpsTests, PWT_Tests_2) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(2.0);
auto y = NDArrayFactory::create<float>('c', {5, 5});
y.assign(3.0);
auto exp = NDArrayFactory::create<float>('c', {5, 5});
exp.assign(6.0);
nd4j::ops::LegacyPairwiseTransformOp op(pairwise::Multiply); // Multiply
auto result = op.execute({&x, &y}, {}, {});
auto z = result->at(0);
//z->printBuffer("Z");
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(LegacyOpsTests, Scalar_Test_1) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(2.0);
auto exp = NDArrayFactory::create<float>('c', {5, 5});
exp.assign(7.0);
nd4j::ops::LegacyScalarOp op(scalar::Add);
op.execute({&x}, {&x}, {5.0}, {}, {}); //
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(LegacyOpsTests, Scalar_Test_2) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(2.0);
auto exp = NDArrayFactory::create<float>('c', {5, 5});
exp.assign(7.0);
auto y = NDArrayFactory::create<float>(5.0f);
nd4j::ops::LegacyScalarOp op(scalar::Add, y);
auto result = op.execute({&x}, {}, {});
auto z = result->at(0);
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(LegacyOpsTests, ReduceTests_1) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(1.0);
int opNum = reduce::Sum;
nd4j::ops::LegacyReduceSameOp op(opNum);
auto result = op.execute({&x}, {}, {});
ASSERT_EQ(1, result->size());
auto z = result->at(0);
z->printBuffer("ReduceTest1");
ASSERT_TRUE(z->isScalar());
ASSERT_NEAR(x.sumNumber().e<float>(0), z->e<float>(0), 1e-5f);
delete result;
}
TEST_F(LegacyOpsTests, ReduceTests_2) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(1.0);
nd4j::ops::LegacyReduceSameOp op(reduce::Sum);
auto axis = NDArrayFactory::create<Nd4jLong>('c', {1}, {1});
auto result = op.execute({&x, &axis}, {}, {});
ASSERT_EQ(1, result->size());
auto z = result->at(0);
auto exp = x.reduceAlongDimension(reduce::Sum, {1});
ASSERT_TRUE(exp->isSameShape(z));
ASSERT_TRUE(exp->equalsTo(z));
delete result;
delete exp;
}
TEST_F(LegacyOpsTests, ReduceTests_3) {
auto x = NDArrayFactory::create<float>('c', {3, 5});
x.linspace(1);
auto indices = NDArrayFactory::create<int>('c', {1,1}, {1});
nd4j::ops::LegacyReduceSameOp op(reduce::Sum);
auto result = op.execute({&x, &indices}, {}, {});
auto z = result->at(0);
auto exp = x.reduceAlongDims(reduce::Sum,{1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(LegacyOpsTests, ReduceTests_4) {
auto x = NDArrayFactory::create<float>('c', {2, 3, 5});
x.linspace(1);
auto indices = NDArrayFactory::create<int>('c', {1, 1}, {1});
nd4j::ops::LegacyReduceSameOp op(reduce::Sum);
auto result = op.execute({&x, &indices}, {}, {}, {true});
auto z = result->at(0);
auto exp = x.reduceAlongDims(reduce::Sum, {1}, true);
indices.printShapeInfo("Indices shape");
ASSERT_EQ(ND4J_STATUS_OK, result->status());
z->printIndexedBuffer("Output reduce 4");
exp.printIndexedBuffer("Expected reduce 4");
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(LegacyOpsTests, ReduceTests_5) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(1.0);
int opNum = reduce::Mean;
nd4j::ops::LegacyReduceFloatOp op(opNum);
ResultSet* result = op.execute({&x}, {}, {}, {}, false, nd4j::DataType::FLOAT32);
ASSERT_EQ(1, result->size());
auto z = result->at(0);
z->printBuffer("ReduceTest1");
ASSERT_TRUE(z->isScalar());
ASSERT_NEAR(x.meanNumber().e<float>(0), z->e<float>(0), 1e-5f);
delete result;
}
TEST_F(LegacyOpsTests, ReduceTests_6) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.assign(1.0);
auto axis = NDArrayFactory::create<int>('c', {1}, {1});
nd4j::ops::LegacyReduceFloatOp op(reduce::Mean);
auto result = op.execute({&x, &axis}, {}, {});
ASSERT_EQ(1, result->size());
auto z = result->at(0);
auto exp = x.reduceAlongDimension(reduce::Mean, {1});
ASSERT_TRUE(exp->isSameShape(z));
ASSERT_TRUE(exp->equalsTo(z));
delete result;
delete exp;
}
TEST_F(LegacyOpsTests, ReduceTests_7) {
auto x = NDArrayFactory::create<float>('c', {3, 5});
x.linspace(1);
auto indices = NDArrayFactory::create<int>('c', {1,1}, {1});
nd4j::ops::LegacyReduceFloatOp op(reduce::Mean);
auto result = op.execute({&x, &indices}, {}, {});
auto z = result->at(0);
auto exp = x.reduceAlongDims(reduce::Mean,{1});
ASSERT_EQ(ND4J_STATUS_OK, result->status());
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(LegacyOpsTests, ReduceTests_8) {
auto x = NDArrayFactory::create<float>('c', {2, 3, 5});
x.linspace(1);
auto indices = NDArrayFactory::create<int>('c', {1}, {1});
nd4j::ops::LegacyReduceFloatOp op(reduce::Mean);
auto result = op.execute({&x, &indices}, {}, {}, {true});
auto z = result->at(0);
auto exp = x.reduceAlongDims(reduce::Mean, {1}, true);
ASSERT_EQ(ND4J_STATUS_OK, result->status());
z->printIndexedBuffer("Reduce8 output");
z->printShapeInfo("Reduce8 shape");
exp.printShapeInfo("Reduce8 expected shape");
ASSERT_TRUE(exp.isSameShape(z));
ASSERT_TRUE(exp.equalsTo(z));
delete result;
}
TEST_F(LegacyOpsTests, IndexReduceTests_1) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
x.linspace(1);
nd4j::ops::LegacyIndexReduceOp op(indexreduce::IndexMax);
auto result = op.execute({&x}, {}, {});
ASSERT_EQ(1, result->size());
auto z = result->at(0);
ASSERT_TRUE(z->isScalar());
ASSERT_EQ(24, z->e<int>(0));
delete result;
}
TEST_F(LegacyOpsTests, IndexReduceTests_2) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
auto indices = NDArrayFactory::create<int>('c', {1}, {1});
x.linspace(1);
auto exp = NDArrayFactory::create<Nd4jLong>({4,4,4,4,4});
nd4j::ops::LegacyIndexReduceOp op(indexreduce::IndexMax);
auto result = op.execute({&x, &indices}, {}, {});
ASSERT_EQ(1, result->size());
auto z = result->at(0);
z->printIndexedBuffer("Hello indexreduce2");
ASSERT_TRUE(exp.equalsTo(z));
//ASSERT_EQ(4, z->e<int>(0));
//ASSERT_EQ(4, z->e<int>(1));
//ASSERT_EQ(4, z->e<int>(2));
//ASSERT_EQ(4, z->e<int>(3));
//ASSERT_EQ(4, z->e<int>(4));
delete result;
}
TEST_F(LegacyOpsTests, Test_IsMax_1) {
if (!Environment::getInstance()->isCPU())
return;
auto x = NDArrayFactory::create<double>('c', {2, 2, 2, 2, 2, 2});
auto z = NDArrayFactory::create<double>('c', {2, 2, 2, 2, 2, 2});
x.linspace(1.0);
z.assign(-589);
double extra[] = {1.0, 0.0};
NativeOpExecutioner::execTransformAny(nullptr, transform::IsMax, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), extra, nullptr, nullptr);
// z.printIndexedBuffer("z");
for (int e = 0; e < z.lengthOf(); e++) {
ASSERT_TRUE(z.e<double>(e) >= 0);
}
}
TEST_F(LegacyOpsTests, Test_IsMax_2) {
if (!Environment::getInstance()->isCPU())
return;
auto x = NDArrayFactory::create<double>('c', {2, 2, 2, 2, 2, 2});
auto z = NDArrayFactory::create<bool>('c', {2, 2, 2, 2, 2, 2});
x.linspace(1.0);
z.assign(false);
double extra[] = {1.0, 0.0};
NativeOpExecutioner::execTransformAny(nullptr, transform::IsMax, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), extra, nullptr, nullptr);
// z.printIndexedBuffer("z");
for (int e = 0; e < z.lengthOf(); e++) {
if (e >= z.lengthOf() / 2)
ASSERT_TRUE(z.e<bool>(e));
else
ASSERT_FALSE(z.e<bool>(e));
}
}
TEST_F(LegacyOpsTests, BroadcastingTests_1) {
auto x = NDArrayFactory::create<double>('c', {5, 5});
x.assign(0.0f);
auto row = NDArrayFactory::create<double>('c', {1, 5});
row.linspace(1);
auto axis = NDArrayFactory::create<int>('c', {1}, {1});
nd4j::ops::LegacyBroadcastOp op(broadcast::Add);
Nd4jStatus status = op.execute({&x, &row, &axis}, {&x}, {}, {}, {});
ASSERT_EQ(ND4J_STATUS_OK, status);
auto list = x.allTensorsAlongDimension({1});
x.printIndexedBuffer("Output broadcast");
list->at(0)->printIndexedBuffer("Column 0:");
for (int e = 0; e < list->size(); e++)
ASSERT_TRUE(row.equalsTo(list->at(e)));
delete list;
}
TEST_F(LegacyOpsTests, BroadcastingTests_2) {
auto x = NDArrayFactory::create<double>('c', {5}, {1, 1, 1, 1, 1});
auto y = NDArrayFactory::create<double>('c', {10, 5});
auto e = NDArrayFactory::create<double>('c', {10, 5});
y.assign(3.0);
e.assign(4.0);
int axis = 1;
shape::TAD tad;
tad.init(y.shapeInfo(), &axis, 1);
tad.createTadOnlyShapeInfo();
tad.createOffsets();
shape::printShapeInfoLinear("tad shape", tad.tadOnlyShapeInfo);
NativeOpExecutioner::execInverseBroadcast(LaunchContext::defaultContext(), broadcast::Add, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), &axis, 1, tad.tadOnlyShapeInfo, tad.tadOffsets, tad.tadOnlyShapeInfo, tad.tadOffsets);
ASSERT_EQ(e, y);
}
TEST_F(LegacyOpsTests, PowDerivative_1) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
auto exp = NDArrayFactory::create<float>('c', {5, 5});
x.assign(3.f);
exp.assign(6.f);
float p = 2.0f;
x.applyScalar(scalar::PowDerivative, p);
ASSERT_TRUE(exp.equalsTo(&x));
}
TEST_F(LegacyOpsTests, reduce3_1) {
Nd4jLong yShape[2] = {4,4};
Nd4jLong xShape[1] = {4};
float y[16] ={1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16};
float x[4] = {1,2,3,4};
int dimension[1] = {1};
int dimensionLength = 1;
int opNum = 1;
float extraVals[1] = {0};
float result[4] = {0.0,0.0,0.0,0.0};
std::vector<int> dim = {1};
auto shapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 2, yShape);
auto xShapeBuffer = nd4j::ShapeBuilders::createShapeInfo(nd4j::DataType::FLOAT32, 'c', 1, xShape);
//int *tadShapeBuffer = shape::computeResultShape(shapeBuffer,dimension,dimensionLength);
auto tadShapeBuffer = nd4j::ShapeUtils::evalReduceShapeInfo('c', dim, shapeBuffer, false, true, nullptr);
functions::reduce3::Reduce3<float, float>::exec(opNum, x, xShapeBuffer, extraVals, y, shapeBuffer, result, tadShapeBuffer, dimension, dimensionLength);
float distancesAssertion[4] = {0.0,8.0,16.0,24.0};
for(int i = 0; i < 4; i++)
ASSERT_EQ(distancesAssertion[i],result[i]);
delete[] shapeBuffer;
delete[] xShapeBuffer;
}
TEST_F(LegacyOpsTests, Reduce3_2) {
auto x = NDArrayFactory::create<float>('c', {5, 5});
auto y = NDArrayFactory::create<float>('c', {5});
auto z = NDArrayFactory::create<float>('c', {5});
auto dim = NDArrayFactory::create<int>('c', {1}, {1});
NativeOps nativeOps;
nativeOps.execReduce3(nullptr, reduce3::CosineSimilarity, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(), z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), dim.buffer(), dim.shapeInfo(), dim.specialBuffer(), dim.specialShapeInfo(),
nullptr, nullptr, nullptr, nullptr);
}
TEST_F(LegacyOpsTests, Reduce3_3) {
auto x = NDArrayFactory::create<double>('c', {3, 5}, {-0.84443557262, -0.06822254508, 0.74266910552, 0.61765557527, -0.77555125951,
-0.99536740779, -0.0257304441183, -0.6512106060, -0.345789492130, -1.25485503673,
0.62955373525, -0.31357592344, 1.03362500667, -0.59279078245, 1.1914824247});
auto y = NDArrayFactory::create<double>('c', {5}, {-0.99536740779, -0.0257304441183, -0.6512106060, -0.345789492130, -1.25485503673});
auto e = NDArrayFactory::create<double>('c', {3}, {0.577452, 0.0, 1.80182});
auto z = NDArrayFactory::create<double>('c', {3});
auto dim = NDArrayFactory::create<int>('c', {1}, {1});
NativeOps nativeOps;
nativeOps.execReduce3(nullptr, reduce3::CosineDistance,
x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
nullptr,
y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(),
z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(),
dim.buffer(), dim.shapeInfo(), dim.specialBuffer(), dim.specialShapeInfo(),
nullptr, nullptr, nullptr, nullptr);
// z.printIndexedBuffer("z");
ASSERT_EQ(e, z);
}
TEST_F(LegacyOpsTests, Reduce3_4) {
auto x = NDArrayFactory::create<double>('c', {3, 5}, {-0.84443557262, -0.06822254508, 0.74266910552, 0.61765557527, -0.77555125951,
-0.99536740779, -0.0257304441183, -0.6512106060, -0.345789492130, -1.25485503673,
0.62955373525, -0.31357592344, 1.03362500667, -0.59279078245, 1.1914824247});
auto y = NDArrayFactory::create<double>('c', {1, 5}, {-0.99536740779, -0.0257304441183, -0.6512106060, -0.345789492130, -1.25485503673});
auto e = NDArrayFactory::create<double>('c', {1, 3}, {0.577452, 0.0, 1.80182});
auto z = NDArrayFactory::create<double>('c', {1, 3});
auto dim = NDArrayFactory::create<int>('c', {1}, {1});
NativeOps nativeOps;
nativeOps.execReduce3(nullptr, reduce3::CosineDistance,
x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
nullptr,
y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(),
z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(),
dim.buffer(), dim.shapeInfo(), dim.specialBuffer(), dim.specialShapeInfo(),
nullptr, nullptr, nullptr, nullptr);
// z.printIndexedBuffer("z");
ASSERT_EQ(e, z);
}
TEST_F(LegacyOpsTests, Reduce3_5) {
auto x = NDArrayFactory::create<double>('c', {3, 5}, {-0.84443557262, -0.06822254508, 0.74266910552, 0.61765557527, -0.77555125951,
-0.99536740779, -0.0257304441183, -0.6512106060, -0.345789492130, -1.25485503673,
0.62955373525, -0.31357592344, 1.03362500667, -0.59279078245, 1.1914824247});
auto y = NDArrayFactory::create<double>('c', {1, 5}, {-0.99536740779, -0.0257304441183, -0.6512106060, -0.345789492130, -1.25485503673});
auto e = NDArrayFactory::create<double>('c', {1, 3}, {0.577452, 0.0, 1.80182});
auto z = NDArrayFactory::create<double>('c', {1, 3});
auto dim = NDArrayFactory::create<int>('c', {1}, {1});
NativeOps nativeOps;
nativeOps.execReduce3(nullptr, reduce3::CosineDistance,
x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
nullptr,
y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(),
z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(),
dim.buffer(), dim.shapeInfo(), dim.specialBuffer(), dim.specialShapeInfo(),
nullptr, nullptr, nullptr, nullptr);
z.printIndexedBuffer("z");
ASSERT_EQ(e, z);
}
TEST_F(LegacyOpsTests, test_Reduce3_All_1) {
auto x = NDArrayFactory::create<float>('c', {1000, 100});
auto y = NDArrayFactory::create<float>('c', {1, 100});
auto z = NDArrayFactory::create<float>('c', {1000, 1});
auto dim = NDArrayFactory::create<int>('c', {1}, {-1});
auto tadPackX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(x.shapeInfo(), -1);
auto tadPackY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), -1);
NativeOps ops;
ops.execReduce3All(nullptr, reduce3::EuclideanDistance, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
nullptr, y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(),
z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(),
dim.buffer(), dim.shapeInfo(), dim.specialBuffer(), dim.specialShapeInfo(),
tadPackX.platformShapeInfo(), tadPackX.platformOffsets(),
tadPackY.platformShapeInfo(), tadPackY.platformOffsets());
}
TEST_F(LegacyOpsTests, test_inverse_broadcast_1) {
auto x = NDArrayFactory::create<float>('c', {4}, {2.0f, 2.0f, 2.0f, 2.0f});
auto y = NDArrayFactory::create<float>('c', {3, 4});
auto e = NDArrayFactory::create<float>('c', {3, 4});
e.assign(2.0f);
auto tadPackY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), 1);
y.tickWriteDevice();
NativeOpExecutioner::execInverseBroadcast(LaunchContext::defaultContext(), broadcast::Add,
x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(),
y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(),
nullptr, 0,
tadPackY.platformShapeInfo(), tadPackY.platformOffsets(),
tadPackY.platformShapeInfo(), tadPackY.platformOffsets());
ASSERT_EQ(e, y);
}
TEST_F(LegacyOpsTests, test_inverse_broadcast_2) {
auto x = NDArrayFactory::create<float>('c', {4}, {2.0f, 2.0f, 2.0f, 2.0f});
auto y = NDArrayFactory::create<float>('c', {3, 4});
auto z = NDArrayFactory::create<bool>('c', {3, 4});
auto e = NDArrayFactory::create<bool>('c', {3, 4});
e.assign(false);
auto row = y.tensorAlongDimension(1, {1});
row->assign(2.0f);
auto erow = e.tensorAlongDimension(1, {1});
erow->assign(true);
auto tadPackY = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(y.shapeInfo(), 1);
y.tickWriteDevice();
NativeOpExecutioner::execInverseBroadcastBool(LaunchContext::defaultContext(), broadcast::BoolOps::EqualTo,
x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(),
y.buffer(), y.shapeInfo(), y.specialBuffer(), y.specialShapeInfo(),
z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(),
nullptr, 0,
tadPackY.platformShapeInfo(), tadPackY.platformOffsets(),
tadPackY.platformShapeInfo(), tadPackY.platformOffsets());
ASSERT_EQ(e, z);
delete row;
delete erow;
Dev branch merge: dev_20190606 (#7904) * correct logsoftmax looss (#2) * Small SameDiff listener fix (#4) * Various fixes (#6) * #7839 Fix for asXMatrix and tests * #7866 EmbeddingSequenceLayer dtype fix + test * #7856 SameDiff save/load stream methods * #7859 RegressionEvaluation rank 4 fix + tests + axis configuration * EvaluationBinary 3d/4d * More evaluation 3d/4d tests * #7847 Evaluation empty checks * Small test ifx * #7848 Fix median edge case * Improve DL4J samediff layer tests * [WIP] FastText wrapper implemented (#8) * FastText implemented * Some fixes * Fix shapes for wordsNearest * Validation of input vectors * Fixes * Fixed test * Thread tagged * Some tweaks * setContextClassLoader for DeallocatorServiceThread * Numpy format tests (#1) * Various fixes (#11) * #7852 SameDiff gather fix * #7892 SameDiff placeholder to constant conversion * #7890 validate input rank for MLN/CG init methods * Fix broken permute shape calculation * Permute and gather fixes * Tests * #7850 LogSumExp fix + test * Handful of test fixes * Empty arrays with non-scalar shapes (#10) * minor rearrangements for lambdas * empty tensors with non-scalar shapes * numpy empty tensors with non-scalar shapes * few more empty tweaks * Small fixes * conv3d signature update * micro fix in batchnorm mkldnn * Import fixes * Fix * MKL-DNN update * Small fill fix * fill with empty input + test * Fixes * Small error improvement * Fix * one special test * couple of fixes for lstm * Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone * Fixes * FP16 * Unsigned * BFloat16 * Fill op - empty tweaks * - couple of fixes for empty arrays construction - stack updated * strided slice fix * one transform test * provide method for reducing shapeInfo in case of input array is empty * Fixed reduceAlongDimensions to use empty input properly. * couple of broadcast tests * couple of tests broadcast tests + tweak to make them pass * add check of non-empty to methods producing sub-arrays * Fixed reshapeC with zeros in shape. * complete empty check in reduce_... legacy ops * Concat and cumsum/prod * Tweak to empty shape inference on import * add empty check to the rest of reduce legacy ops * one more test * correct typo in evalReduceShapeInfoEmpty * Added tests for reduce_* ops to tests with zero shapes. * few more tests for empty reductions * Fixed strided_slice op with empty case and tests. * one more empty reduction test * Fixed strided_slice test. * add empty check to NDArray::reshapei * infOrMax * empty min/max with infinity tests * made unstack working correctly with empty arrays * few IndexReduce tests + tweaks for empty shapes * add test for empty concat * few tests fixed * Validation fix for reductions on empty shapes * Reverse fix * Reduction shape calc fixes * SameDiff.generateOutputVariable: don't use shape function to determine number of outputs * Range fix * - NDArray constructor updated for scalars/empty arrays - few tests fixed * More fixes * Empty creator fixes * concat fix * concat fix * TF import tests: allow 'both all NaN' and 'both all inf' to pass * Slice, zero fraction, and reshape fixes * transpose, gather * Zero fraction * scalar cast fix * Empty reduction axis support * few more tests fixed * Fixed input checks conforming with TF for concat op and tests. * few tests fixed * matmul scalar shape fix * Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats. * broadcast bool fix * few more tests * few more tests * correct evalReduceShapeInfoEmpty * argmax/argmin + tests * one more empty edge case + one more test * argmax/argmin/realdiv_bp tweaks * empty reshape test + fix * Helper fixes * Small fixes * Gather test fix * Gather test fix * Small fixes * reduce scalar zero values * scalar mean workaround * Remove debug code * along dim mean workaround * one more test * - equalsTo() tweak for empty arrays - one more test * broadcast tweaks
2019-06-15 13:34:34 +02:00
}
TEST_F(LegacyOpsTests, test_legacy_reduce_empty_1) {
auto x = NDArrayFactory::create<float>('c', {2, 0, 3});
auto z = NDArrayFactory::create<float>('c', {2, 3});
auto e = NDArrayFactory::create<float>('c', {2, 3});
int dim = 1;
NativeOpExecutioner::execReduceSame(LaunchContext::defaultContext(), reduce::SameOps::Sum, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), &dim, 1, x.shapeInfo(), nullptr);
ASSERT_EQ(e, z);
}
TEST_F(LegacyOpsTests, test_legacy_reduce_empty_2) {
auto x = NDArrayFactory::create<float>('c', {2, 0, 3});
auto z = NDArrayFactory::create<float>('c', {2, 3});
auto e = NDArrayFactory::create<float>('c', {2, 3});
e.assign(std::numeric_limits<float>::infinity());
int dim = 1;
NativeOpExecutioner::execReduceSame(LaunchContext::defaultContext(), reduce::SameOps::Min, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), &dim, 1, x.shapeInfo(), nullptr);
ASSERT_EQ(e, z);
}
TEST_F(LegacyOpsTests, test_legacy_reduce_empty_3) {
auto x = NDArrayFactory::create<float>('c', {2, 0, 3});
auto z = NDArrayFactory::create<float>('c', {2, 3});
auto e = NDArrayFactory::create<float>('c', {2, 3});
e.assign(-std::numeric_limits<float>::infinity());
int dim = 1;
NativeOpExecutioner::execReduceSame(LaunchContext::defaultContext(), reduce::SameOps::Max, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, z.buffer(), z.shapeInfo(), z.specialBuffer(), z.specialShapeInfo(), &dim, 1, x.shapeInfo(), nullptr);
ASSERT_EQ(e, z);
}
TEST_F(LegacyOpsTests, test_legacy_transform_float_1) {
auto x = NDArrayFactory::create<float>('c', {1, 0, 4});
NativeOpExecutioner::execTransformFloat(LaunchContext::defaultContext(), transform::FloatOps::RSqrt, x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), x.buffer(), x.shapeInfo(), x.specialBuffer(), x.specialShapeInfo(), nullptr, nullptr, nullptr);
Eclipse Migration Initial Commit
2019-06-06 14:21:15 +02:00
}