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Shugeo bug3 #6906

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merged 7 commits into from Dec 19, 2018
Merged

Shugeo bug3 #6906

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5798637
Fixed axes and bool params for reduce_dot/min/max ops.
shugeo Dec 19, 2018
f765f98
Fixed norm1 and norm1_bp reducing ops.
shugeo Dec 19, 2018
12eaf76
Fixed reduce norms and reduce_prod.
shugeo Dec 19, 2018
494a451
Fixed reduce_sum and reduce_sum_bp to use proper axis.
shugeo Dec 19, 2018
00ea721
Fixed reduce_*_bp ops and tests.
shugeo Dec 19, 2018
967273d
Merge branch 'master' of https://github.com/deeplearning4j/deeplearni…
shugeo Dec 19, 2018
13f8aea
Fixed some ops.
shugeo Dec 19, 2018
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93 changes: 78 additions & 15 deletions libnd4j/blas/cpu/NDArray.cpp
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Expand Up @@ -4032,8 +4032,11 @@ template <typename T>
NDArray NDArray::operator+(const T& scalar) const {
if (isS())
throw std::runtime_error("NDArray::operator+: you can't use this method on String array!");

auto tmp = NDArrayFactory::create(this->dataType(), scalar, _workspace);
DataType type;
if (!Environment::getInstance()->isExperimentalBuild()){
type = this->dataType();
}
auto tmp = NDArrayFactory::create(type, scalar, _workspace);
NDArray result(_shapeInfo, DataTypeUtils::pickPairwiseResultType(_dataType, DataTypeUtils::fromT<T>()), false, _workspace);
NativeOpExcutioner::execScalar(nd4j::scalar::Add, _buffer, _shapeInfo, result._buffer, result._shapeInfo, tmp.buffer(), tmp.shapeInfo(), nullptr);
return result;
Expand All @@ -4056,7 +4059,12 @@ NDArray NDArray::operator-(const T& scalar) const {
if (isS())
throw std::runtime_error("NDArray::operator-: you can't use this method on String array!");

auto tmp = NDArrayFactory::create(scalar);
DataType type;
if (!Environment::getInstance()->isExperimentalBuild()){
type = this->dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, _workspace);
NDArray result(_shapeInfo, DataTypeUtils::pickPairwiseResultType(_dataType, DataTypeUtils::fromT<T>()), false, _workspace);
NativeOpExcutioner::execScalar(nd4j::scalar::Subtract, _buffer, _shapeInfo, result._buffer, result._shapeInfo, tmp.buffer(), tmp.shapeInfo(), nullptr);
return result;
Expand All @@ -4079,7 +4087,12 @@ NDArray NDArray::operator*(const T& scalar) const {
if (isS())
throw std::runtime_error("NDArray::operator*: you can't use this method on String array!");

auto tmp = NDArrayFactory::create(this->dataType(), scalar, _workspace);
DataType type = DataTypeUtils::fromT<T>();
if (!Environment::getInstance()->isExperimentalBuild()){
type = this->dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, _workspace);
NDArray result(_shapeInfo, DataTypeUtils::pickPairwiseResultType(_dataType, DataTypeUtils::fromT<T>()), false, _workspace);
NativeOpExcutioner::execScalar(nd4j::scalar::Multiply, _buffer, _shapeInfo, result._buffer, result._shapeInfo, tmp.buffer(), tmp.shapeInfo(), nullptr);
return result;
Expand All @@ -4105,7 +4118,12 @@ NDArray NDArray::operator/(const T& scalar) const {
if(scalar == (T)0.)
throw std::runtime_error("NDArray::operator/ (division operator) : division by zero !");

auto tmp = NDArrayFactory::create(this->dataType(), scalar, _workspace);
DataType type;
if (!Environment::getInstance()->isExperimentalBuild()){
type = this->dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, _workspace);
NDArray result(_shapeInfo, DataTypeUtils::pickPairwiseResultType(_dataType, DataTypeUtils::fromT<T>()), false, _workspace);
NativeOpExcutioner::execScalar(nd4j::scalar::Divide, _buffer, _shapeInfo, result._buffer, result._shapeInfo, tmp.getBuffer(), tmp.getShapeInfo(), nullptr);
return result;
Expand Down Expand Up @@ -4169,7 +4187,12 @@ ND4J_EXPORT NDArray operator-(const float16& scalar, const NDArray & arr) {
if (arr.isS())
throw std::runtime_error("NDArray::operator-: you can't use this method on String array!");

auto tmp = NDArrayFactory::create(scalar, arr.getWorkspace());
DataType type = DataTypeUtils::fromT<float16>();
if (!Environment::getInstance()->isExperimentalBuild()){
type = arr.dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, arr.getWorkspace());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<float16>()), false, arr.getWorkspace());
NativeOpExcutioner::execScalar(nd4j::scalar::ReverseSubtract, arr.getBuffer(), arr.getShapeInfo(), result.getBuffer(), result.getShapeInfo(), tmp.getBuffer(), tmp.getShapeInfo(), nullptr);
return result;
Expand All @@ -4180,7 +4203,12 @@ ND4J_EXPORT NDArray operator-(const bfloat16& scalar, const NDArray & arr) {
if (arr.isS())
throw std::runtime_error("NDArray::operator-: you can't use this method on String array!");

auto tmp = NDArrayFactory::create(scalar, arr.getWorkspace());
DataType type = DataTypeUtils::fromT<bfloat16>();
if (!Environment::getInstance()->isExperimentalBuild()){
type = arr.dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, arr.getWorkspace());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<bfloat16>()), false, arr.getWorkspace());
NativeOpExcutioner::execScalar(nd4j::scalar::ReverseSubtract, arr.getBuffer(), arr.getShapeInfo(), result.getBuffer(), result.getShapeInfo(), tmp.getBuffer(), tmp.getShapeInfo(), nullptr);
return result;
Expand All @@ -4190,7 +4218,12 @@ ND4J_EXPORT NDArray operator-(const float& scalar, const NDArray& arr) {
if (arr.isS())
throw std::runtime_error("NDArray::operator-: you can't use this method on String array!");

auto tmp = NDArrayFactory::create(arr.dataType(), scalar, arr.getWorkspace());
DataType type = DataTypeUtils::fromT<float>();
if (!Environment::getInstance()->isExperimentalBuild()){
type = arr.dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, arr.getWorkspace());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<float>()), false, arr.getWorkspace());
NativeOpExcutioner::execScalar(nd4j::scalar::ReverseSubtract, arr.getBuffer(), arr.getShapeInfo(), result.getBuffer(), result.getShapeInfo(), tmp.getBuffer(), tmp.getShapeInfo(), nullptr);
return result;
Expand All @@ -4213,7 +4246,7 @@ ND4J_EXPORT NDArray operator-(const Nd4jLong& scalar, const NDArray& arr) {
if (arr.isS())
throw std::runtime_error("NDArray::operator-: you can't use this method on String array!");

DataType type = DataTypeUtils::fromT<double>();
DataType type = DataTypeUtils::fromT<Nd4jLong>();
if (!Environment::getInstance()->isExperimentalBuild()) {
type = arr.dataType();
}
Expand All @@ -4227,7 +4260,12 @@ ND4J_EXPORT NDArray operator-(const int& scalar, const NDArray& arr) {
if (arr.isS())
throw std::runtime_error("NDArray::operator-: you can't use this method on String array!");

auto tmp = NDArrayFactory::create(scalar, arr.getWorkspace());
DataType type = DataTypeUtils::fromT<int>();
if (!Environment::getInstance()->isExperimentalBuild()){
type = arr.dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, arr.getWorkspace());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<int>()), false, arr.getWorkspace());
NativeOpExcutioner::execScalar(nd4j::scalar::ReverseSubtract, arr.getBuffer(), arr.getShapeInfo(), result.getBuffer(), result.getShapeInfo(), tmp.getBuffer(), tmp.getShapeInfo(), nullptr);
return result;
Expand All @@ -4241,7 +4279,12 @@ ND4J_EXPORT NDArray operator/(const bfloat16& scalar, const NDArray& arr) {
if (arr.isB())
throw std::runtime_error("NDArray::operator/: you can't divide scalar by bool array!");

auto tmp = NDArrayFactory::create(scalar, arr.getWorkspace());
DataType type = DataTypeUtils::fromT<bfloat16>();
if (!Environment::getInstance()->isExperimentalBuild()){
type = arr.dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, arr.getWorkspace());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<bfloat16>()), false, arr.getWorkspace());
NativeOpExcutioner::execScalar(nd4j::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), result.getBuffer(), result.getShapeInfo(), tmp.getBuffer(), tmp.getShapeInfo(), nullptr);
return result;
Expand All @@ -4253,7 +4296,12 @@ ND4J_EXPORT NDArray operator/(const float16& scalar, const NDArray& arr) {
if (arr.isB())
throw std::runtime_error("NDArray::operator/: you can't divide scalar by bool array!");

auto tmp = NDArrayFactory::create(scalar, arr.getWorkspace());
DataType type = DataTypeUtils::fromT<float16>();
if (!Environment::getInstance()->isExperimentalBuild()){
type = arr.dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, arr.getWorkspace());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<float16>()), false, arr.getWorkspace());
NativeOpExcutioner::execScalar(nd4j::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), result.getBuffer(), result.getShapeInfo(), tmp.getBuffer(), tmp.getShapeInfo(), nullptr);
return result;
Expand All @@ -4265,7 +4313,12 @@ ND4J_EXPORT NDArray operator/(const float& scalar, const NDArray & arr) {
if (arr.isB())
throw std::runtime_error("NDArray::operator/: you can't divide scalar by bool array!");

auto tmp = NDArrayFactory::create(scalar, arr.getWorkspace());
DataType type = DataTypeUtils::fromT<float>();
if (!Environment::getInstance()->isExperimentalBuild()){
type = arr.dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, arr.getWorkspace());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<float>()), false, arr.getWorkspace());
NativeOpExcutioner::execScalar(nd4j::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), result.getBuffer(), result.getShapeInfo(), tmp.getBuffer(), tmp.getShapeInfo(), nullptr);
return result;
Expand All @@ -4277,7 +4330,12 @@ ND4J_EXPORT NDArray operator/(const double& scalar, const NDArray & arr) {
if (arr.isB())
throw std::runtime_error("NDArray::operator/: you can't divide scalar by bool array!");

auto tmp = NDArrayFactory::create(scalar, arr.getWorkspace());
DataType type = DataTypeUtils::fromT<double>();
if (!Environment::getInstance()->isExperimentalBuild()){
type = arr.dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, arr.getWorkspace());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<double>()), false, arr.getWorkspace());
NativeOpExcutioner::execScalar(nd4j::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), result.getBuffer(), result.getShapeInfo(), tmp.getBuffer(), tmp.getShapeInfo(), nullptr);
return result;
Expand All @@ -4289,7 +4347,12 @@ ND4J_EXPORT NDArray operator/(const int& scalar, const NDArray & arr) {
if (arr.isB())
throw std::runtime_error("NDArray::operator/: you can't divide scalar by bool array!");

auto tmp = NDArrayFactory::create(scalar, arr.getWorkspace());
DataType type = DataTypeUtils::fromT<int>();
if (!Environment::getInstance()->isExperimentalBuild()){
type = arr.dataType();
}

auto tmp = NDArrayFactory::create(type, scalar, arr.getWorkspace());
NDArray result(arr.getShapeInfo(), DataTypeUtils::pickPairwiseResultType(arr.dataType(), DataTypeUtils::fromT<int>()), false, arr.getWorkspace());
NativeOpExcutioner::execScalar(nd4j::scalar::ReverseDivide, arr.getBuffer(), arr.getShapeInfo(), result.getBuffer(), result.getShapeInfo(), tmp.getBuffer(), tmp.getShapeInfo(), nullptr);
return result;
Expand Down
54 changes: 40 additions & 14 deletions libnd4j/include/ops/declarable/generic/parity_ops/reduceMean.cpp
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Expand Up @@ -20,7 +20,7 @@


#include <ops/declarable/CustomOperations.h>

#include <ops/declarable/helpers/axis.h>

namespace nd4j {
namespace ops {
Expand All @@ -30,9 +30,17 @@ CUSTOM_OP_IMPL(reduce_mean, 1, 1, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);

const bool keepDims = block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;

std::vector<int> dimensions = *block.getIArguments();
auto dimensions = *block.getIArguments();
if (block.width() > 1) {
auto axesVector = INPUT_VARIABLE(1);
helpers::adjustAxis(input, axesVector, dimensions);
}
// else if (block.getIArguments()->size())
bool keepDims = false;
if (block.getBArguments()->size())
keepDims = B_ARG(0);
else if (block.getTArguments()->size())
keepDims = (bool)T_ARG(0);

REQUIRE_TRUE(dimensions.size() <= input->rankOf(), 0, "REDUCE_MEAN OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());

Expand All @@ -51,10 +59,18 @@ CUSTOM_OP_IMPL(reduce_mean, 1, 1, false, 0, 0) {
->setAllowedOutputTypes({ALL_FLOATS});
}

DECLARE_SHAPE_FN(reduce_mean) {
const bool keepDims = block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;

std::vector<int> dimensions = *block.getIArguments();
DECLARE_SHAPE_FN(reduce_mean) {
auto dimensions = *block.getIArguments();
if (block.width() > 1) {
auto axesVector = INPUT_VARIABLE(1);
helpers::adjustAxis(INPUT_VARIABLE(0), axesVector, dimensions);
}
// else if (block.getIArguments()->size())
bool keepDims = false;
if (block.getBArguments()->size())
keepDims = B_ARG(0);
else if (block.getTArguments()->size())
keepDims = (bool)T_ARG(0);

REQUIRE_TRUE(dimensions.size() <= inputShape->at(0)[0], 0, "REDUCE_MEAN OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());

Expand All @@ -81,9 +97,17 @@ CUSTOM_OP_IMPL(reduce_mean_bp, 2, 1, false, 0, 0) {

auto gradI = OUTPUT_VARIABLE(0);

const bool keepDims = block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;

std::vector<int> dimensions = *block.getIArguments();
auto dimensions = *block.getIArguments();
if (block.width() > 2) {
auto axesVector = INPUT_VARIABLE(2);
helpers::adjustAxis(input, axesVector, dimensions);
}
// else if (block.getIArguments()->size())
bool keepDims = false;
if (block.getBArguments()->size())
keepDims = B_ARG(0);
else if (block.getTArguments()->size())
keepDims = (bool)T_ARG(0);

REQUIRE_TRUE(dimensions.size() <= input->rankOf(), 0, "REDUCE_MEAN_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());

Expand Down Expand Up @@ -115,9 +139,11 @@ CUSTOM_OP_IMPL(reduce_mean_bp, 2, 1, false, 0, 0) {


DECLARE_SHAPE_FN(reduce_mean_bp) {
const bool keepDims = block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;

std::vector<int> dimensions = *block.getIArguments();
auto dimensions = *block.getIArguments();
if (block.width() > 2) {
auto axesVector = INPUT_VARIABLE(2);
helpers::adjustAxis(INPUT_VARIABLE(0), axesVector, dimensions);
}

REQUIRE_TRUE(dimensions.size() <= inputShape->at(0)[0], 0, "REDUCE_MEAN_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead" , dimensions.size());

Expand Down