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cavis/libnd4j/blas/cpu/NDArrayLambda.hpp
Alex Black 68ea5f3688
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 21:34:34 +10:00

325 lines
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template<typename T>
void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<T(T, T, T)>& func, NDArray* target) {
if (target == nullptr)
target = this;
if (second == nullptr) {
nd4j_printf("applyTriplewiseLambda requires three operands to be valid NDArrays, but Second is NULL\n","");
throw std::runtime_error("second is null");
}
if (third == nullptr) {
nd4j_printf("applyTriplewiseLambda requires three operands to be valid NDArrays, but Third is NULL\n","");
throw std::runtime_error("third is null");
}
if(dataType() != DataTypeUtils::fromT<T>())
throw std::runtime_error("NDArray::applyTriplewiseLambda<T> method: wrong template parameter T, its type should be the same as type of this array!");
if(dataType() != second->dataType() || dataType() != third->dataType() || dataType() != target->dataType())
throw std::runtime_error("NDArray::applyTriplewiseLambda<T> method: bother four arrays (this, second, third, target) should have the same type !");
if (this->lengthOf() != second->lengthOf() || this->lengthOf() != third->lengthOf() || !this->isSameShape(second) || !this->isSameShape(third)) {
nd4j_printf("applyPairwiseLambda requires both operands to have the same shape\n","");
throw std::runtime_error("Shapes mismach");
}
auto f = this->bufferAsT<T>();
auto s = second->bufferAsT<T>();
auto t = third->bufferAsT<T>();
auto z = target->bufferAsT<T>();
if (this->ordering() == second->ordering() && this->ordering() == third->ordering() && this->ordering() == target->ordering() && (this->ews() == 1 && target->ews() == 1) && this->ews() == second->ews() && this->ews() == third->ews()) {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (Nd4jLong e = 0; e < _length; e++)
z[e] = func(f[e], s[e], t[e]);
} else {
if (f == z) {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < _length; e++) {
auto tOffset = this->getOffset(e);
auto uOffset = second->getOffset(e);
auto vOffset = third->getOffset(e);
f[tOffset] = func(f[tOffset], s[uOffset], t[vOffset]);
}
} else {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < _length; e++) {
auto tOffset = this->getOffset(e);
auto uOffset = second->getOffset(e);
auto vOffset = third->getOffset(e);
auto zOffset = target->getOffset(e);
z[zOffset] = func(f[tOffset], s[uOffset], t[vOffset]);
}
}
}
}
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<double (double, double, double)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<float (float, float, float)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<float16 (float16, float16, float16)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<bfloat16 (bfloat16, bfloat16, bfloat16)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<Nd4jLong (Nd4jLong, Nd4jLong, Nd4jLong)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<int (int, int, int)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<int16_t (int16_t, int16_t, int16_t)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<uint8_t (uint8_t, uint8_t, uint8_t)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<uint16_t (uint16_t, uint16_t, uint16_t)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<uint32_t (uint32_t, uint32_t, uint32_t)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<uint64_t (uint64_t, uint64_t, uint64_t)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<int8_t (int8_t, int8_t, int8_t)>& func, NDArray* target);
template void NDArray::applyTriplewiseLambda(NDArray* second, NDArray *third, const std::function<bool (bool, bool, bool)>& func, NDArray* target);
//////////////////////////////////////////////////////////////////////////
template<typename T>
void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<T(T, T)>& func, NDArray* target) {
if (target == nullptr)
target = this;
if (other == nullptr) {
nd4j_printf("applyPairwiseLambda requires both operands to be valid NDArrays, but Y is NULL\n","");
throw std::runtime_error("Other is null");
}
if(dataType() != DataTypeUtils::fromT<T>())
throw std::runtime_error("NDArray::applyPairwiseLambda<T> method: wrong template parameter T, its type should be the same as type of this array!");
if(dataType() != other->dataType() || dataType() != target->dataType())
throw std::runtime_error("NDArray::applyPairwiseLambda<T> method: all three arrays (this, other, target) must have the same type !");
if (this->lengthOf() != other->lengthOf()) {
nd4j_printf("applyPairwiseLambda requires both operands to have the same shape\n","");
throw std::runtime_error("Shapes mismach");
}
auto f = this->bufferAsT<T>();
auto s = other->bufferAsT<T>();
auto z = target->bufferAsT<T>();
if (this->ordering() == other->ordering() && this->ordering() == target->ordering() && (this->ews() == 1 && target->ews() == 1) && this->ews() == other->ews()) {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < _length; e++)
z[e] = func(f[e], s[e]);
} else {
if (f == z) {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < _length; e++) {
auto xOffset = this->getOffset(e);
auto yOffset = other->getOffset(e);
f[xOffset] = func(f[xOffset], s[yOffset]);
}
} else {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < _length; e++) {
auto xOffset = this->getOffset(e);
auto yOffset = other->getOffset(e);
auto zOffset = target->getOffset(e);
z[zOffset] = func(f[xOffset], s[yOffset]);
}
}
}
}
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<double (double, double)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<float (float, float)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<float16 (float16, float16)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<bfloat16 (bfloat16, bfloat16)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<Nd4jLong (Nd4jLong, Nd4jLong)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<int (int, int)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<int16_t (int16_t, int16_t)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<uint8_t (uint8_t, uint8_t)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<uint16_t (uint16_t, uint16_t)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<uint32_t (uint32_t, uint32_t)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<uint64_t (uint64_t, uint64_t)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<int8_t (int8_t, int8_t)>& func, NDArray* target);
template void NDArray::applyPairwiseLambda(const NDArray* other, const std::function<bool (bool, bool)>& func, NDArray* target);
//////////////////////////////////////////////////////////////////////////
template<typename T>
void NDArray::applyLambda(const std::function<T(T)>& func, NDArray* target) {
if (target == nullptr)
target = this;
if(dataType() != DataTypeUtils::fromT<T>())
throw std::runtime_error("NDArray::applyLambda<T> method: wrong template parameter T, its type should be the same as type of this array!");
if(dataType() != target->dataType())
throw std::runtime_error("NDArray::applyLambda<T> method: types of this and target array should match !");
auto f = this->bufferAsT<T>();
auto z = target->bufferAsT<T>();
if (this->ordering() == target->ordering() && (this->ews() == 1 && target->ews() == 1)) {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < _length; e++)
z[e] = func(f[e]);
} else {
if (f == z) {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < _length; e++) {
auto xOffset = this->getOffset(e);
f[xOffset] = func(f[xOffset]);
}
} else {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < _length; e++) {
auto xOffset = this->getOffset(e);
auto zOffset = target->getOffset(e);
z[zOffset] = func(f[xOffset]);
}
}
}
}
template void NDArray::applyLambda(const std::function<double(double)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<float(float)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<float16(float16)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<bfloat16(bfloat16)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<Nd4jLong(Nd4jLong)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<int16_t(int16_t)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<int32_t(int32_t)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<uint8_t(uint8_t)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<uint16_t(uint16_t)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<uint32_t(uint32_t)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<uint64_t(uint64_t)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<int8_t(int8_t)>& func, NDArray* target);
template void NDArray::applyLambda(const std::function<bool(bool)>& func, NDArray* target);
//////////////////////////////////////////////////////////////////////////
template<typename T>
void NDArray::applyIndexedLambda(const std::function<T(Nd4jLong, T)>& func, NDArray* target) {
if (target == nullptr)
target = this;
if(dataType() != DataTypeUtils::fromT<T>())
throw std::runtime_error("NDArray::applyIndexedLambda<T> method: wrong template parameter T, its type should be the same as type of this array!");
if(dataType() != target->dataType())
throw std::runtime_error("NDArray::applyIndexedLambda<T> method: types of this and target array should match !");
auto f = this->bufferAsT<T>();
auto z = target->bufferAsT<T>();
if (this->ordering() == target->ordering() && (this->ews() == 1 && target->ews() == 1)) {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (Nd4jLong e = 0; e < _length; e++)
z[e] = func(e, f[e]);
} else {
if (f == z) {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (Nd4jLong e = 0; e < _length; e++) {
auto xOffset = this->getOffset(e);
f[xOffset] = func(e, f[xOffset]);
}
} else {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (Nd4jLong e = 0; e < _length; e++) {
auto xOffset = this->getOffset(e);
auto zOffset = target->getOffset(e);
z[zOffset] = func(e, f[xOffset]);
}
}
}
}
template void NDArray::applyIndexedLambda(const std::function<double(Nd4jLong, double)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<float(Nd4jLong, float)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<float16(Nd4jLong, float16)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<bfloat16(Nd4jLong, bfloat16)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<Nd4jLong(Nd4jLong, Nd4jLong)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<int(Nd4jLong, int)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<int16_t(Nd4jLong, int16_t)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<uint8_t (Nd4jLong, uint8_t)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<uint16_t (Nd4jLong, uint16_t)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<uint32_t (Nd4jLong, uint32_t)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<uint64_t (Nd4jLong, uint64_t)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<int8_t(Nd4jLong, int8_t)>& func, NDArray* target);
template void NDArray::applyIndexedLambda(const std::function<bool(Nd4jLong, bool)>& func, NDArray* target);
//////////////////////////////////////////////////////////////////////////
template<typename T>
void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<T(Nd4jLong, T, T)>& func, NDArray* target) {
if (target == nullptr)
target = this;
if (other == nullptr) {
nd4j_printf("applyIndexedPairwiseLambda requires both operands to be valid NDArrays, but Y is NULL\n","");
throw std::runtime_error("Other is null");
}
if(dataType() != DataTypeUtils::fromT<T>())
throw std::runtime_error("NDArray::applyIndexedPairwiseLambda<T> method: wrong template parameter T, its type should be the same as type of this array!");
if(dataType() != target->dataType())
throw std::runtime_error("NDArray::applyIndexedPairwiseLambda<T> method: types of this and target array should match !");
if (this->lengthOf() != other->lengthOf()) {
nd4j_printf("applyIndexedPairwiseLambda requires both operands to have the same shape\n","");
throw std::runtime_error("Shapes mismach");
}
auto f = this->bufferAsT<T>();
auto s = other->bufferAsT<T>();
auto z = target->bufferAsT<T>();
if (this->ordering() == other->ordering() && this->ordering() == target->ordering() && (this->ews() == 1 && target->ews() == 1) && this->ews() == other->ews()) {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (Nd4jLong e = 0; e < _length; e++)
z[e] = func((Nd4jLong) e, f[e], s[e]);
} else {
if (f == z) {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < _length; e++) {
auto xOffset = this->getOffset(e);
auto yOffset = other->getOffset(e);
f[xOffset] = func((Nd4jLong) e, f[xOffset], s[yOffset]);
}
} else {
PRAGMA_OMP_PARALLEL_FOR_SIMD
for (int e = 0; e < _length; e++) {
auto xOffset = this->getOffset(e);
auto yOffset = other->getOffset(e);
auto zOffset = target->getOffset(e);
z[zOffset] = func((Nd4jLong) e, f[xOffset], s[yOffset]);
}
}
}
}
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<double (Nd4jLong, double, double)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<float (Nd4jLong, float, float)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<float16 (Nd4jLong, float16, float16)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<bfloat16 (Nd4jLong, bfloat16, bfloat16)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<Nd4jLong (Nd4jLong, Nd4jLong, Nd4jLong)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<int (Nd4jLong, int, int)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<int16_t (Nd4jLong, int16_t, int16_t)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<uint8_t (Nd4jLong, uint8_t, uint8_t)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<uint16_t (Nd4jLong, uint16_t, uint16_t)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<uint32_t (Nd4jLong, uint32_t, uint32_t)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<uint64_t (Nd4jLong, uint64_t, uint64_t)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<int8_t (Nd4jLong, int8_t, int8_t)>& func, NDArray* target);
template void NDArray::applyIndexedPairwiseLambda(NDArray* other, const std::function<bool (Nd4jLong, bool, bool)>& func, NDArray* target);
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