cavis/libnd4j/include/ops/declarable/generic/broadcastable/divide.cpp

145 lines
4.8 KiB
C++

/*******************************************************************************
* 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_divide)
#include <ops/declarable/generic/helpers/BroadcastHelper.h>
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
namespace ops {
BROADCASTABLE_OP_IMPL(divide, 0, 0) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
BROADCAST_CHECK_EMPTY(x,y,z);
REQUIRE_TRUE(!y->isB(), 0, "DIVIDE OP: you can't divide by bool array!");
auto tZ = BroadcastHelper::broadcastApply(BroadcastOpsTuple::Divide(), x, y, z);
if (tZ == nullptr)
return ND4J_STATUS_KERNEL_FAILURE;
else if (tZ != z) {
OVERWRITE_RESULT(tZ);
}
return Status::OK();
}
DECLARE_SYN(Div, divide);
DECLARE_TYPES(divide) {
getOpDescriptor()
->setAllowedInputTypes(0, DataType::ANY)
->setAllowedInputTypes(1, DataType::ANY)
->setAllowedOutputTypes(0, DataType::INHERIT);
}
DECLARE_TYPES(divide_bp) {
getOpDescriptor()
->setAllowedInputTypes(DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
CUSTOM_OP_IMPL(divide_bp, 3, 2, false, 0, 0) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto epsNext = INPUT_VARIABLE(2);
auto gradX = OUTPUT_VARIABLE(0);
auto gradY = OUTPUT_VARIABLE(1);
/*
auto lambdaY = LAMBDA_TTT(_e, _x, _y) {
return _e * -_x / (_y * _y);
};
*/
if (x->isSameShape(y)) {
// PWT case case
// X gradient
//epsNext->applyPairwiseLambda(y, lambdaX, gradX);
gradX->assign((*epsNext) / (*y));
// Y gradient
//epsNext->applyTriplewiseLambda(x, y, lambdaY, gradY);
gradY->assign((*epsNext) * (*x) / ((*y) * (*y)));
gradY->applyTransform(transform::Neg, *gradY);
} else if (y->isScalar()) {
// scalar case
auto tmp = epsNext->reduceNumber(reduce::Sum);
auto tmpX = x->reduceNumber(reduce::Sum);
//tmpX.printBuffer("SumX");
//tmp.printBuffer("Sum Eps");
gradY->assign(tmp * tmpX / ((*y) * (*y)));
gradY->applyTransform(transform::Neg, *gradY);
//epsNext->applyLambda(lambdaS, *gradX);
epsNext->applyScalarArr(scalar::Divide, *y, *gradX);
} else {
// broadcast case
auto preX = *epsNext / *y;
NDArray negX(*x);
x->applyTransform(transform::Neg, negX);
auto preY = *epsNext * negX / ((*y) * (*y));
auto axisX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), epsNext->shapeInfo());
auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());
if (axisX.size() > 0) {
auto sum = preX.reduceAlongDimension(reduce::Sum, axisX);
gradX->assign(sum);
} else
gradX->assign(preX);
if (axisY.size() > 0) {
auto sum = preY.reduceAlongDimension(reduce::Sum, axisY);
gradY->assign(sum);
} else
gradY->assign(preY);
}
return Status::OK();
}
DECLARE_SHAPE_FN(divide_bp) {
auto x = inputShape->at(0);
auto y = inputShape->at(1);
auto e = inputShape->at(2);
// eps always has shape of x
// grad always has shape of y
Nd4jLong *shapeE;
Nd4jLong *shapeG;
COPY_SHAPE(x, shapeE);
COPY_SHAPE(y, shapeG);
return SHAPELIST(CONSTANT(shapeE), CONSTANT(shapeG));
}
}
}
#endif