/*******************************************************************************
* 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
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_log_poisson_loss)
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
namespace ops {
CUSTOM_OP_IMPL(log_poisson_loss, 3, 1, true, 0, 1) {
auto log_predictions = INPUT_VARIABLE(0);
auto weights = INPUT_VARIABLE(1);
auto labels = INPUT_VARIABLE(2);
auto output = OUTPUT_VARIABLE(0);
int reductionMode = INT_ARG(0); // 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
bool computeFullLoss = false;
if (block.numI() > 1)
computeFullLoss = INT_ARG(1) != 0;
// inputs validation
REQUIRE_TRUE(labels->isSameShape(log_predictions), 0, "LOG_POISSON_LOSS OP: labels and log_predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(log_predictions).c_str());
// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == labels->rankOf(), 0, "LOG_POISSON_LOSS OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", weights->rankOf(), labels->rankOf());
// check whether broadcast operation is possible for weights array
REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *labels), 0, "LOG_POISSON_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
// only 4 possible reduction modes exist
REQUIRE_TRUE(reductionMode==0 || reductionMode==1 || reductionMode==2 || reductionMode==3, 0, "LOG_POISSON_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);
// perform weights broadcasting/tile to labels if needed
auto weightsBroad = weights;
if(!weights->isScalar() && !weights->isSameShape(log_predictions))
weightsBroad = new NDArray(weights->tileToShape(log_predictions->getShapeInfo()));
NDArray E(labels->getShapeInfo(), block.getWorkspace());
if (computeFullLoss)
labels->applyPairwiseTransform(pairwise::LogPoissonLossFull, log_predictions, &E, nullptr);
else
labels->applyPairwiseTransform(pairwise::LogPoissonLoss, log_predictions, &E, nullptr);
// multiply E on weights
E *= *weightsBroad;
switch (reductionMode) {
case 0: { // 0 - "none", un-reduced weighted losses with the same shape as labels.
output->assign(E);
break;
}
case 1: { // 1 - "weighted_sum", output is scalar and equal to sum of all elements of E array
E.reduceNumber(reduce::Sum, *output);
break;
}
case 2: { // 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of all elements of weightsBroad array
NDArray sum;
if (weights->isScalar())
sum = *weights * E.lengthOf();
else
sum = weightsBroad->reduceNumber(reduce::Sum);
if (sum.e<double>(0) == 0.)
*output = 0.;
else
output->assign(E.reduceNumber(reduce::Sum) / sum);
break;
}
case 3: { // 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E array divided by number of non-zero weights
Nd4jLong numOfNonZeroWeights = 0;
if(weights->isScalar()) {
if(weights->e<double>(0) != 0.)
numOfNonZeroWeights = E.lengthOf();
}
else {
numOfNonZeroWeights = weightsBroad->reduceNumber(reduce::CountNonZero).e<Nd4jLong>(0);
}
if (numOfNonZeroWeights == 0)
(*output) = 0.;
else
output->assign(E.reduceNumber(reduce::Sum) / double(numOfNonZeroWeights));
break;
}
}
if(weightsBroad != weights)
delete weightsBroad;
return Status::OK();
}
//////////////////////////////////////////////////////////////////////////
DECLARE_TYPES(log_poisson_loss) {
getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}
//////////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(log_poisson_loss) {
auto predictionsShapeInfo = inputShape->at(0);
auto weightsShapeInfo = inputShape->at(1);
auto labelsShapeInfo = inputShape->at(2);
// labels and predictions must have the same shapes
REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0, "LOG_POISSON_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(predictionsShapeInfo).c_str());
// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(labelsShapeInfo), 0, "LOG_POISSON_LOSS OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(labelsShapeInfo));
// check whether broadcast operation is possible for weights array
REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0, "LOG_POISSON_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(labelsShapeInfo).c_str());
DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(predictionsShapeInfo));
Nd4jLong* outShapeInfo = nullptr;
if(INT_ARG(0) != 0) // in this case output is scalar
outShapeInfo = ConstantShapeHelper::getInstance()->scalarShapeInfo(outType);
else // in this case output has the same shape as labels and predictions
outShapeInfo = ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(labelsShapeInfo, outType));
return SHAPELIST(outShapeInfo);
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(log_poisson_loss_grad, 3, 3, false, 0, 1) {
auto log_predictions = INPUT_VARIABLE(0);
auto weights = INPUT_VARIABLE(1);
auto labels = INPUT_VARIABLE(2);
auto dLdp = OUTPUT_VARIABLE(0); // dL/dpredictions
auto dLdw = OUTPUT_VARIABLE(1); // dL/dweights
auto dLdl = OUTPUT_VARIABLE(2); // dL/dlabels
int reductionMode = INT_ARG(0); // 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
// take into account Alex's proposition to treat "none" the same as "weighted_sum" mode when calculating gradients
if(reductionMode == 0)
reductionMode = 1;
bool computeFullLoss = false;
if (block.numI() > 1)
computeFullLoss = INT_ARG(1) != 0;
// inputs validation
REQUIRE_TRUE(labels->isSameShape(log_predictions), 0, "LOG_POISSON_LOSS_GRAD OP: labels and log_predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(log_predictions).c_str());
// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == labels->rankOf(), 0, "LOG_POISSON_LOSS_GRAD OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", weights->rankOf(), labels->rankOf());
// check whether broadcast operation is possible for weights array
REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *labels), 0, "LOG_POISSON_LOSS_GRAD OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
// only 4 possible reduction modes exist
REQUIRE_TRUE(reductionMode==0 || reductionMode==1 || reductionMode==2 || reductionMode==3, 0, "LOG_POISSON_LOSS_GRAD OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);
// perform weights broadcasting/tile to labels if needed
auto weightsBroad = weights;
if(!weights->isScalar() && !weights->isSameShape(log_predictions))
weightsBroad = new NDArray(weights->tileToShape(log_predictions->getShapeInfo()));
NDArray E(labels->getShapeInfo(), block.getWorkspace());
if (computeFullLoss) {
labels->applyPairwiseTransform(pairwise::LogPoissonLossFull, log_predictions, &E, nullptr);
NDArray rDiv(labels->getShapeInfo(), block.getWorkspace());
labels->applyScalar(scalar::ReverseDivide, 0.5f, &rDiv);
dLdl->assign(rDiv + labels->transform(transform::Log) + -(*log_predictions));
} else {
labels->applyPairwiseTransform(pairwise::LogPoissonLoss, log_predictions, &E, nullptr);
dLdl->assign(-(*log_predictions));
}
dLdp->assign(log_predictions->transform(transform::Exp) - (*labels));
switch (reductionMode) {
case 1: { // 1 - "none" and "weighted_sum", output is scalar and equal to sum of all elements of E array
*dLdp *= *weightsBroad;
*dLdl *= *weightsBroad;
if(weights->isScalar())
dLdw->assign(E.reduceNumber(reduce::Sum));
else if(weights != weightsBroad) {
std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->getShapeInfo(), weightsBroad->getShapeInfo());
E.reduceAlongDimension(reduce::Sum, dLdw, axesToReduceAlong, true, false, false);
}
else
dLdw->assign(E);
break;
}
case 2: { // 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of all elements of weightsBroad array
NDArray sum;
if (weights->isScalar())
sum = (*weights) * E.lengthOf();
else
sum = weightsBroad->reduceNumber(reduce::Sum);
if (sum.e<double>(0) == 0.) {
*dLdp = 0.;
*dLdl = 0.;
*dLdw = 0.;
}
else {
*dLdp *= *weightsBroad / sum;
*dLdl *= *weightsBroad / sum;
if(weights->isScalar())
*dLdw = 0.;
else if(weights != weightsBroad) {
std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->getShapeInfo(), weightsBroad->getShapeInfo());
((E * sum - (E * *weightsBroad).reduceNumber(reduce::Sum)) / (sum*sum)).reduceAlongDimension(reduce::Sum, dLdw, axesToReduceAlong, true, false, false);
}
else
dLdw->assign((E * sum - (E * *weightsBroad).reduceNumber(reduce::Sum)) / (sum*sum));
}
break;
}
case 3: { // 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E array divided by number of non-zero weights
Nd4jLong numOfNonZeroWeights = 0;
if(weights->isScalar()) {
if(weights->e<double>(0) != 0.)
numOfNonZeroWeights = E.lengthOf();
}
else
numOfNonZeroWeights = weightsBroad->reduceNumber(reduce::CountNonZero).e<Nd4jLong>(0);
if (numOfNonZeroWeights == 0) {
*dLdp = 0.;
*dLdl = 0.;
*dLdw = 0.;
}
else {
auto numOfNonZeroWeightsScalar = NDArrayFactory::create(dLdw->dataType(), numOfNonZeroWeights, block.launchContext());
if(weights->isScalar())
dLdw->assign(E.reduceNumber(reduce::Sum) / double(numOfNonZeroWeights));
else if(weights != weightsBroad) {
std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->getShapeInfo(), weightsBroad->getShapeInfo());
E.reduceAlongDimension(reduce::Sum, dLdw, axesToReduceAlong, true, false, false);
*dLdw /= numOfNonZeroWeightsScalar;
}
else
dLdw->assign(E / numOfNonZeroWeightsScalar);
NDArray temp = *weightsBroad / numOfNonZeroWeightsScalar;
*dLdp *= temp;
*dLdl *= temp;
}
break;
}
}
if(weightsBroad != weights)
delete weightsBroad;
return Status::OK();
}
DECLARE_TYPES(log_poisson_loss_grad) {
getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(log_poisson_loss_grad) {
auto predictionsShapeInfo = inputShape->at(0);
auto weightsShapeInfo = inputShape->at(1);
auto labelsShapeInfo = inputShape->at(2);
// labels and predictions must have the same shapes
REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0, "LOG_POISSON_LOSS_GRAD OP: labels and predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(predictionsShapeInfo).c_str());
// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(labelsShapeInfo), 0, "LOG_POISSON_LOSS_GRAD OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(labelsShapeInfo));
// check whether broadcast operation is possible for weights array
REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0, "LOG_POISSON_LOSS_GRAD OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(labelsShapeInfo).c_str());
DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(predictionsShapeInfo));
Nd4jLong *dLdpShapeInfo = ShapeBuilders::copyShapeInfoAndType(predictionsShapeInfo, outType, false, block.getWorkspace());
Nd4jLong *dLdwShapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, outType, false, block.getWorkspace());
Nd4jLong *dLdlShapeInfo = ShapeBuilders::copyShapeInfoAndType(labelsShapeInfo, outType, false, block.getWorkspace());
return SHAPELIST(dLdpShapeInfo, dLdwShapeInfo, dLdlShapeInfo);
}
}
}
#endif