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

157 lines
5.1 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
******************************************************************************/
//
// Created by raver119 on 23.11.17.
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_squaredsubtract)
#include <ops/declarable/generic/helpers/BroadcastHelper.h>
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
namespace ops {
BROADCASTABLE_OP_IMPL(squaredsubtract, 0, 0) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
BROADCAST_CHECK_EMPTY(x,y,z);
auto tZ = BroadcastHelper::broadcastApply(BROADCAST(SquaredSubtract), x, y, z);
if (tZ == nullptr)
return ND4J_STATUS_KERNEL_FAILURE;
else if (tZ != z) {
OVERWRITE_RESULT(tZ);
}
return Status::OK();
}
DECLARE_SYN(squareddifference, squaredsubtract);
DECLARE_TYPES(squaredsubtract) {
getOpDescriptor()
->setAllowedInputTypes(0, DataType::ANY)
->setAllowedInputTypes(1, DataType::ANY)
->setAllowedOutputTypes(0, DataType::INHERIT);
}
CUSTOM_OP_IMPL(squaredsubtract_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 lambdaX = LAMBDA_TTT(_e, _x, _y) {
return _e * (T) 2.0 * (_x - _y) ;
};
auto lambdaY = LAMBDA_TTT(_e, _x, _y) {
return _e * (T) 2.0 * (_y - _x);
};
*/
auto ts = NDArrayFactory::create(x->dataType(), 2, block.launchContext());
if (x->isSameShape(y)) {
// PWT case case
// X gradient
//epsNext->applyTriplewiseLambda(x, y, lambdaX, gradX);
gradX->assign((*epsNext) * ts * ((*x) - (*y)));
// Y gradient
//epsNext->applyTriplewiseLambda(x, y, lambdaY, gradY);
gradY->assign((*epsNext) * ts * ((*y) - (*x)));
} else if (y->isScalar()) {
// scalar case
auto tmpX = x->reduceNumber(reduce::Sum);
gradY->assign(tmpX);
//epsNext->applyPairwiseLambda(x, lambdaS, gradX);
gradX->assign((*epsNext) * ts * ((*x) - (*y)));
} else {
// broadcast case
auto preX = x->dup();
auto preY = y->dup();
auto targetShape = epsNext->getShapeAsVector();
preX.tileToShape(targetShape, preX);
preY.tileToShape(targetShape, preY);
//epsNext->applyTriplewiseLambda(x, y, lambdaX, preX);
//epsNext->applyTriplewiseLambda(x, y, lambdaY, preY);
auto resX = (*epsNext) * ts * ((*x) - (*y));
preX.assign(resX);
auto resY = (*epsNext) * ts * ((*y) - (*x));
preY.assign(resY);
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(squaredsubtract_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));
}
DECLARE_TYPES(squaredsubtract_bp) {
getOpDescriptor()
->setAllowedInputTypes(DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
}
}
#endif