/*******************************************************************************
* 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
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_multiply)
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
namespace ops {
BROADCASTABLE_OP_IMPL(multiply, 0, 0) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
BROADCAST_CHECK_EMPTY(x,y,z);
Nd4jLong* zShapeInfo = nullptr;
const bool areShapesBroadcastable = ShapeUtils::evalBroadcastShapeInfo(x->getShapeInfo(), y->getShapeInfo(), true, zShapeInfo, block.getWorkspace());
REQUIRE_TRUE(areShapesBroadcastable, 0, "MULTIPLY OP: the shapes of x %s and y %s are not suitable for broadcast !", ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str());
auto tZ = BroadcastHelper::broadcastApply(nd4j::BroadcastOpsTuple::Multiply(), x, y, z);
if (tZ == nullptr)
return ND4J_STATUS_KERNEL_FAILURE;
else if (tZ != z)
throw std::runtime_error("multiply: result was replaced");
return Status::OK();
}
DECLARE_SYN(Mul, multiply);
DECLARE_TYPES(multiply) {
getOpDescriptor()
->setAllowedInputTypes(0, DataType::ANY)
->setAllowedInputTypes(1, DataType::ANY)
->setAllowedOutputTypes(0, DataType::INHERIT);
}
DECLARE_TYPES(multiply_bp) {
getOpDescriptor()
->setAllowedInputTypes(DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
///////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(multiply_bp, 3, 2, false, 0, 0) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto dLdz = INPUT_VARIABLE(2);
auto dLdx = OUTPUT_VARIABLE(0);
auto dLdy = OUTPUT_VARIABLE(1);
Nd4jLong* dLdzShapeInfo = nullptr;
const bool areShapesBroadcastable = ShapeUtils::evalBroadcastShapeInfo(x->getShapeInfo(), y->getShapeInfo(), true, dLdzShapeInfo, block.getWorkspace());
REQUIRE_TRUE(areShapesBroadcastable, 0, "MULTIPLY_BP OP: the shapes of x %s and y %s are not suitable for broadcast !", ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str());
REQUIRE_TRUE(shape::equalsSoft(dLdz->shapeInfo(), dLdzShapeInfo), 0, "MULTIPLY_BP OP: wrong shape of next epsilon array (dLdOut), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(dLdzShapeInfo).c_str(), ShapeUtils::shapeAsString(dLdz).c_str());
const Nd4jLong xLen = x->lengthOf();
const Nd4jLong yLen = y->lengthOf();
if(x->isScalar() && y->isScalar()) { // both are scalars
y->applyPairwiseTransform(pairwise::Multiply, dLdz, dLdx, nullptr);
x->applyPairwiseTransform(pairwise::Multiply, dLdz, dLdy, nullptr);
//dLdx->assign((*y) * (*dLdz));
//dLdy->assign((*x) * (*dLdz));
}
else if(x->isScalar()) { // x is scalar and y is not
dLdx->assign((*y * *dLdz).reduceNumber(reduce::Sum));
dLdz->applyScalarArr(scalar::Multiply, x, dLdy, nullptr);
//dLdz->applyTrueBroadcast(broadcast::Multiply, x, dLdy, true);
}
else if(y->isScalar()) { // y is scalar and x is not
dLdy->assign((*x * *dLdz).reduceNumber(reduce::Sum));
dLdz->applyScalarArr(scalar::Multiply, y, dLdx);
}
else if(x->isSameShape(y)) {
x->applyPairwiseTransform(pairwise::Multiply, dLdz, dLdy, nullptr);
y->applyPairwiseTransform(pairwise::Multiply, dLdz, dLdx, nullptr);
}
else if (x->isSameShape(dLdz)) {
auto yTiled = NDArray(dLdz, false, block.launchContext());
y->tile(yTiled);
std::vector<int> axesForY = ShapeUtils::evalBroadcastBackwardAxis(y->getShapeInfo(), dLdz->getShapeInfo());
dLdy->assign( (*x * *dLdz).reduceAlongDims(reduce::Sum, axesForY) );
yTiled.applyPairwiseTransform(pairwise::Multiply, dLdz, dLdx, nullptr);
}
else if (y->isSameShape(dLdz)) {
auto xTiled = NDArray(dLdz, false, block.launchContext());
x->tile(xTiled);
std::vector<int> axesForX = ShapeUtils::evalBroadcastBackwardAxis(x->getShapeInfo(), dLdz->getShapeInfo());
dLdx->assign( (*y * *dLdz).reduceAlongDims(reduce::Sum, axesForX) );
xTiled.applyPairwiseTransform(pairwise::Multiply, dLdz, dLdy, nullptr);
}
else {
auto xTiled = NDArray(dLdz, false, block.launchContext());
auto yTiled = NDArray(dLdz, false, block.launchContext());
x->tile(xTiled);
y->tile(yTiled);
std::vector<int> axesForX = ShapeUtils::evalBroadcastBackwardAxis(x->getShapeInfo(), dLdz->getShapeInfo());
std::vector<int> axesForY = ShapeUtils::evalBroadcastBackwardAxis(y->getShapeInfo(), dLdz->getShapeInfo());
dLdx->assign( (*y * *dLdz).reduceAlongDims(reduce::Sum, axesForX) );
dLdy->assign( (*x * *dLdz).reduceAlongDims(reduce::Sum, axesForY) );
}
return Status::OK();
}
DECLARE_SHAPE_FN(multiply_bp) {
auto xShapeInfo = inputShape->at(0);
auto yShapeInfo = inputShape->at(1);
Nd4jLong *dLdxShapeInfo = nullptr;
Nd4jLong *dLdyShapeInfo = nullptr;
COPY_SHAPE(xShapeInfo, dLdxShapeInfo);
COPY_SHAPE(yShapeInfo, dLdyShapeInfo);
return SHAPELIST(CONSTANT(dLdxShapeInfo), CONSTANT(dLdyShapeInfo));
}
/*
CUSTOM_OP_IMPL(multiply_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_TT(_e, _y) {
return _e * _y;
};
auto lambdaY = LAMBDA_TT(_e, _x) {
return _e * _x;
};
if (x->isSameShape(y)) {
// PWT case case
// X gradient
epsNext->applyPairwiseLambda(y, lambdaX, gradX);
// Y gradient
epsNext->applyPairwiseLambda(x, lambdaY, gradY);
} else if (y->isScalar()) {
// scalar case
T _y = y->e(0);
auto lambdaS = LAMBDA_T(_e, _y) {
return _e * _y;
};
T tmpX = x->template reduceNumber<simdOps::Sum<T>>();
gradY->assign(tmpX);
epsNext->applyLambda(lambdaS, gradX);
} else {
// broadcast case
auto preX = x->dup();
auto preY = y->dup();
auto targetShape = epsNext->getShapeAsVector();
preX->tileToShape(targetShape);
preY->tileToShape(targetShape);
auto axisX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), epsNext->shapeInfo());
auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());
if (axisX.size() > 0) {
auto sum = preX->template reduceAlongDimension<simdOps::Sum<T>>(axisX);
gradX->assign(sum);
delete sum;
} else
gradX->assign(preX);
if (axisY.size() > 0) {
auto sum = preY->template reduceAlongDimension<simdOps::Sum<T>>(axisY);
gradY->assign(sum);
delete sum;
} else
gradY->assign(preY);
delete preX;
delete preY;
}
return Status::OK();
}
*/
}
}
#endif