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cavis/libnd4j/include/ops/declarable/impl/LegacyRandomOp.cpp

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/*******************************************************************************
* 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 <ops/declarable/LegacyRandomOp.h>
#include <helpers/RandomLauncher.h>
#include <NativeOpExecutioner.h>
#include <NDArrayFactory.h>
#include <Status.h>
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
namespace ops {
LegacyRandomOp::LegacyRandomOp() : LegacyOp::LegacyOp(1) {
// just a no-op
}
LegacyRandomOp::LegacyRandomOp(int opNum) : LegacyOp::LegacyOp(1, opNum) {
// just a no-op
}
LegacyOp* LegacyRandomOp::clone() {
return new LegacyRandomOp(this->_opNum);
}
template <typename T>
Nd4jStatus LegacyRandomOp::validateAndExecute_(Context &block) {
auto input = INPUT_VARIABLE(0);
int opNum = block.opNum() < 0 ? this->_opNum : block.opNum();
/*
(0, randomOps::UniformDistribution) ,\
(1, randomOps::DropOut) ,\
(2, randomOps::DropOutInverted) ,\
(3, randomOps::ProbablisticMerge) ,\
(4, randomOps::Linspace) ,\
(5, randomOps::Choice) ,\
(6, randomOps::GaussianDistribution) ,\
(7, randomOps::BernoulliDistribution) ,\
(8, randomOps::BinomialDistribution),\
(9, randomOps::BinomialDistributionEx),\
(10, randomOps::LogNormalDistribution) ,\
(11, randomOps::TruncatedNormalDistribution) ,\
(12, randomOps::AlphaDropOut)
*/
switch(opNum) {
case nd4j::random::UniformDistribution: {
// uniform distribution
T from, to;
if (block.width() > 2) {
auto arg1 = INPUT_VARIABLE(1);
auto arg2 = INPUT_VARIABLE(2);
REQUIRE_TRUE(arg1->isScalar(), 0, "Uniform: Second argument must be scalar");
REQUIRE_TRUE(arg2->isScalar(), 0, "Uniform: Third argument must be scalar");
from = arg1->e<T>(0);
to = arg2->e<T>(0);
} else if (block.getTArguments()->size() == 2) {
from = T_ARG(0);
to = T_ARG(1);
} else {
REQUIRE_TRUE(false, 0, "Uniform requires either TArgs or 3 arguments to be present");
}
auto z = OUTPUT_VARIABLE(0); //NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
RandomLauncher::fillUniform(block.launchContext(), block.randomGenerator(), z, from, to);
// FIXME:
//OVERWRITE_RESULT(z);
}
break;
case nd4j::random::DropOut: {
auto z = OUTPUT_VARIABLE(0);
T prob;
if (block.width() > 1) {
auto arg = INPUT_VARIABLE(1);
REQUIRE_TRUE(arg->isScalar(), 0, "DropOut: Second argument must be scalar");
prob = arg->e<T>(0);
} else if (block.getTArguments()->size() > 0) {
prob = T_ARG(0);
} else {
REQUIRE_TRUE(false, 0, "DropOut requires either TArgs or second argument to be present");
}
if (!block.isInplace())
z->assign(input);
RandomLauncher::applyDropOut(block.launchContext(), block.randomGenerator(), z, prob);
}
break;
case nd4j::random::DropOutInverted: {
auto z = OUTPUT_VARIABLE(0);
nd4j::ops::dropout op;
return op.execute(&block);
}
break;
case nd4j::random::GaussianDistribution: {
// gaussian distribution
T mean, stdev;
if (block.width() > 2) {
auto arg1 = INPUT_VARIABLE(1);
auto arg2 = INPUT_VARIABLE(2);
REQUIRE_TRUE(arg1->isScalar(), 0, "Gaussian: Second argument must be scalar");
REQUIRE_TRUE(arg2->isScalar(), 0, "Gaussian: Third argument must be scalar");
mean = arg1->e<T>(0);
stdev = arg2->e<T>(0);
} else if (block.getTArguments()->size() == 2) {
mean = T_ARG(0);
stdev = T_ARG(1);
} else {
REQUIRE_TRUE(false, 0, "Gaussian requires either TArgs or 3 arguments to be present");
}
REQUIRE_TRUE(input->isVector(), 0, "Gaussian requires pure shape as first argument");
std::vector<Nd4jLong> shape(input->lengthOf());
for (int e = 0; e < input->lengthOf(); e++)
shape[e] = input->e<Nd4jLong>(e);
auto z = OUTPUT_VARIABLE(0);//NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
RandomLauncher::fillGaussian(block.launchContext(), block.randomGenerator(), z, mean, stdev);
// FIXME: !!
//OVERWRITE_RESULT(z);
}
break;
case nd4j::random::BernoulliDistribution: {
// bernoulli distribution
T prob;
if (block.width() > 1) {
auto arg1 = INPUT_VARIABLE(1);
REQUIRE_TRUE(arg1->isScalar(), 0, "Bernoulli: Second argument must be scalar");
prob = arg1->e<T>(0);
} else if (block.getTArguments()->size() > 0) {
prob = T_ARG(0);
} else {
REQUIRE_TRUE(false, 0, "Bernoulli requires either 1 TArg or 2 arguments to be present");
}
REQUIRE_TRUE(input->isVector(), 0, "Bernoulli requires pure shape as first argument");
std::vector<Nd4jLong> shape(input->lengthOf());
for (int e = 0; e < input->lengthOf(); e++)
shape[e] = input->e<Nd4jLong>(e);
auto z = OUTPUT_VARIABLE(0); // NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
RandomLauncher::fillBernoulli(block.launchContext(), block.randomGenerator(), z, prob);
// FIXME:
//OVERWRITE_RESULT(z);
}
break;
case nd4j::random::BinomialDistributionEx: {
// BinomialEx distribution
T prob;
int trials;
if (block.width() > 2) {
auto arg1 = INPUT_VARIABLE(1);
auto arg2 = INPUT_VARIABLE(2);
REQUIRE_TRUE(arg1->isScalar(), 0, "Binomial: Second argument must be scalar");
REQUIRE_TRUE(arg2->isScalar(), 0, "Binomial: Third argument must be scalar");
trials = arg1->e<int>(0);
prob = arg2->e<T>(0);
} else if (block.getTArguments()->size() == 1 && block.getIArguments()->size() == 1) {
trials = INT_ARG(0);
prob = T_ARG(0);
} else {
REQUIRE_TRUE(false, 0, "Binomial requires either TArgs/IArgs or 3 arguments to be present");
}
REQUIRE_TRUE(input->isVector(), 0, "Binomial requires pure shape as first argument");
std::vector<Nd4jLong> shape(input->lengthOf());
for (int e = 0; e < input->lengthOf(); e++)
shape[e] = input->e<Nd4jLong>(e);
auto z = OUTPUT_VARIABLE(0);//NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
RandomLauncher::fillBinomial(block.launchContext(), block.randomGenerator(), z, trials, prob);
// FIXME: !!!
//OVERWRITE_RESULT(z);
}
break;
case nd4j::random::LogNormalDistribution: {
// lognorm distribution
T mean, stdev;
if (block.width() > 2) {
auto arg1 = INPUT_VARIABLE(1);
auto arg2 = INPUT_VARIABLE(2);
REQUIRE_TRUE(arg1->isScalar(), 0, "LogNormal: Second argument must be scalar");
REQUIRE_TRUE(arg2->isScalar(), 0, "LogNormal: Third argument must be scalar");
mean = arg1->e<T>(0);
stdev = arg2->e<T>(0);
} else if (block.getTArguments()->size() == 2) {
mean = T_ARG(0);
stdev = T_ARG(1);
} else {
REQUIRE_TRUE(false, 0, "LogNormal requires either TArgs or 3 arguments to be present");
}
REQUIRE_TRUE(input->isVector(), 0, "LogNormal requires pure shape as first argument");
std::vector<Nd4jLong> shape(input->lengthOf());
for (int e = 0; e < input->lengthOf(); e++)
shape[e] = input->e<Nd4jLong>(e);
auto z = OUTPUT_VARIABLE(0);//NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
RandomLauncher::fillLogNormal(block.launchContext(), block.randomGenerator(), z, mean, stdev);
// FIXME: !!
//OVERWRITE_RESULT(z);
}
break;
case nd4j::random::TruncatedNormalDistribution: {
// truncated norm distribution
T mean, stdev;
if (block.width() > 2) {
auto arg1 = INPUT_VARIABLE(1);
auto arg2 = INPUT_VARIABLE(2);
REQUIRE_TRUE(arg1->isScalar(), 0, "TruncatedNormal: Second argument must be scalar");
REQUIRE_TRUE(arg2->isScalar(), 0, "TruncatedNormal: Third argument must be scalar");
mean = arg1->e<T>(0);
stdev = arg2->e<T>(0);
} else if (block.getTArguments()->size() == 2) {
mean = T_ARG(0);
stdev = T_ARG(1);
} else {
REQUIRE_TRUE(false, 0, "TruncatedNormal requires either TArgs or 3 arguments to be present");
}
REQUIRE_TRUE(input->isVector(), 0, "TruncatedNormal requires pure shape as first argument");
std::vector<Nd4jLong> shape(input->lengthOf());
for (int e = 0; e < input->lengthOf(); e++)
shape[e] = input->e<Nd4jLong>(e);
auto z = OUTPUT_VARIABLE(0); // NDArrayFactory::create_<T>('c', shape, block.getWorkspace());
RandomLauncher::fillTruncatedNormal(block.launchContext(), block.randomGenerator(), z, mean, stdev);
// FIXME: !!!
//OVERWRITE_RESULT(z);
}
break;
case nd4j::random::AlphaDropOut: {
auto z = OUTPUT_VARIABLE(0);
T prob, a, b, pa;
if (block.width() > 4) {
auto arg1 = INPUT_VARIABLE(1);
auto arg2 = INPUT_VARIABLE(2);
auto arg3 = INPUT_VARIABLE(3);
auto arg4 = INPUT_VARIABLE(4);
REQUIRE_TRUE(arg1->isScalar(), 0, "AlphaDropOut: Second argument must be scalar");
REQUIRE_TRUE(arg2->isScalar(), 0, "AlphaDropOut: Third argument must be scalar");
REQUIRE_TRUE(arg3->isScalar(), 0, "AlphaDropOut: Fourth argument must be scalar");
REQUIRE_TRUE(arg4->isScalar(), 0, "AlphaDropOut: Fifth argument must be scalar");
prob = arg1->e<T>(0);
a = arg2->e<T>(0);
b = arg3->e<T>(0);
pa = arg4->e<T>(0);
} else if (block.getTArguments()->size() == 4) {
prob = T_ARG(0);
a = T_ARG(1);
b = T_ARG(2);
pa = T_ARG(3);
} else {
REQUIRE_TRUE(false, 0, "AlphaDropOut requires either TArgs or 5 arguments to be present");
}
if (!block.isInplace())
z->assign(input);
RandomLauncher::applyAlphaDropOut(block.launchContext(), block.randomGenerator(), z, prob, a, b, pa);
}
break;
case nd4j::random::Linspace: {
auto z = OUTPUT_VARIABLE(0);
auto start = INPUT_VARIABLE(0);
auto finish = INPUT_VARIABLE(1);
auto numOfElements = INPUT_VARIABLE(2);
z->linspace(start->e<double>(0), (finish->e<double>(0) - start->e<double>(0)) / (numOfElements->e<Nd4jLong>(0) - 1.));
}
break;
default: {
nd4j_printf("Unknown random op requested: [%i]\n", opNum);
return ND4J_STATUS_KERNEL_FAILURE;
}
}
return Status::OK();
}
Nd4jStatus LegacyRandomOp::validateAndExecute(Context &block) {
// REQUIRE_TRUE(block.getRNG() != nullptr, 0, "RNG should be provided for LegacyRandomOp, but got NULL instead at node_%i", block.nodeId())
auto z = OUTPUT_VARIABLE(0);
BUILD_SINGLE_SELECTOR(z->dataType(), return validateAndExecute_, (block), FLOAT_TYPES);
}
/**
* For transform operations, output shape always equals to input shape. With just a few exclusions, like im2col and col2im.
* But these ops already have CustomOp implementations.
*
*/
ShapeList *LegacyRandomOp::calculateOutputShape(ShapeList *inputShape, nd4j::graph::Context &block) {
auto inShape = inputShape->at(0);
auto xType = ArrayOptions::dataType(inShape);
Nd4jLong *newShape;
if (DataTypeUtils::isR(xType)) {
COPY_SHAPE(inShape, newShape);
return SHAPELIST(newShape);
} else if (DataTypeUtils::isZ(xType)) {
auto zShapeArr = INPUT_VARIABLE(0);
auto zShapeVector = zShapeArr->asVectorT<Nd4jLong>();
auto dtype = block.dataType();
newShape = ConstantShapeHelper::getInstance()->createShapeInfo(dtype, 'c', zShapeVector);
return SHAPELIST(newShape);
} else
throw std::runtime_error("LegacyRandomOp: Unknown input data type!");
}
Nd4jStatus LegacyRandomOp::execute(Context* block) {
return DeclarableOp::execute(block);
}
nd4j::ResultSet* LegacyRandomOp::execute(nd4j::graph::RandomGenerator& rng, std::initializer_list<NDArray*> inputs, std::initializer_list<double> tArgs, std::initializer_list<int> iArgs, bool isInplace) {
std::vector<NDArray*> ins(inputs);
std::vector<double> tas(tArgs);
std::vector<int> ias(iArgs);
return this->execute(rng, ins, tas, ias, isInplace);
}
nd4j::ResultSet* LegacyRandomOp::execute(nd4j::graph::RandomGenerator& rng, std::vector<NDArray*>& inputs, std::vector<double>& tArgs, std::vector<int>& iArgs, bool isInplace) {
VariableSpace variableSpace;
auto arrayList = new ResultSet();
//ResultSet arrayList;
if (isInplace)
arrayList->setNonRemovable();
int cnt = -1;
std::vector<int> in;
for (auto v: inputs) {
if (v == nullptr)
continue;
auto var = new Variable(v);
var->markRemovable(false);
in.push_back(cnt);
variableSpace.putVariable(cnt--, var);
}
Context block(1, &variableSpace, false);
// FIX ME: implement setRng method
block.setRng(rng);
block.fillInputs(in);
block.markInplace(isInplace);
for (int e = 0; e < tArgs.size(); e++)
block.getTArguments()->emplace_back(tArgs.at(e));
for (int e = 0; e < iArgs.size(); e++)
block.getIArguments()->emplace_back(iArgs.at(e));
Nd4jStatus status = this->execute(&block);
arrayList->setStatus(status);
if (status != ND4J_STATUS_OK)
return arrayList;
for (int e = 0; e < DataTypeUtils::max<int>(); e++) {
std::pair<int,int> pair(1, e);
if (variableSpace.hasVariable(pair)) {
auto var = variableSpace.getVariable(pair);
auto arr = var->getNDArray();
if (!arr->isAttached()) {
var->markRemovable(false);
arrayList->push_back(arr);
} else {
arrayList->push_back(arr->detach());
}
} else
break;
}
return arrayList;
}
BUILD_SINGLE_TEMPLATE(template Nd4jStatus LegacyRandomOp::validateAndExecute_, (Context&), FLOAT_TYPES);
}
}
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