147 lines
4.9 KiB
C++
147 lines
4.9 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_divide)
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#include <ops/declarable/generic/helpers/BroadcastHelper.h>
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#include <ops/declarable/CustomOperations.h>
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namespace nd4j {
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namespace ops {
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BROADCASTABLE_OP_IMPL(divide, 0, 0) {
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auto x = INPUT_VARIABLE(0);
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auto y = INPUT_VARIABLE(1);
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auto z = OUTPUT_VARIABLE(0);
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BROADCAST_CHECK_EMPTY(x,y,z);
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REQUIRE_TRUE(!y->isB(), 0, "DIVIDE OP: you can't divide by bool array!");
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auto tZ = BroadcastHelper::broadcastApply(BroadcastOpsTuple::Divide(), x, y, z);
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if (tZ == nullptr)
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return ND4J_STATUS_KERNEL_FAILURE;
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else if (tZ != z) {
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OVERWRITE_RESULT(tZ);
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}
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return Status::OK();
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}
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DECLARE_SYN(Div, divide);
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DECLARE_TYPES(divide) {
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getOpDescriptor()
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->setAllowedInputTypes(0, DataType::ANY)
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->setAllowedInputTypes(1, DataType::ANY)
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->setAllowedOutputTypes(0, DataType::INHERIT);
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}
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DECLARE_TYPES(divide_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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CUSTOM_OP_IMPL(divide_bp, 3, 2, false, 0, 0) {
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auto x = INPUT_VARIABLE(0);
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auto y = INPUT_VARIABLE(1);
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auto epsNext = INPUT_VARIABLE(2);
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auto gradX = OUTPUT_VARIABLE(0);
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auto gradY = OUTPUT_VARIABLE(1);
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/*
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auto lambdaY = LAMBDA_TTT(_e, _x, _y) {
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return _e * -_x / (_y * _y);
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};
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*/
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if (x->isSameShape(y)) {
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// PWT case case
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// X gradient
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//epsNext->applyPairwiseLambda(y, lambdaX, gradX);
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gradX->assign((*epsNext) / (*y));
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// Y gradient
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//epsNext->applyTriplewiseLambda(x, y, lambdaY, gradY);
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gradY->assign((*epsNext) * (*x) / ((*y) * (*y)));
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gradY->applyTransform(transform::Neg, nullptr, nullptr);
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} else if (y->isScalar()) {
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// scalar case
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auto tmp = epsNext->reduceNumber(reduce::Sum);
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auto tmpX = x->reduceNumber(reduce::Sum);
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//tmpX.printBuffer("SumX");
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//tmp.printBuffer("Sum Eps");
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gradY->assign(tmp * tmpX / ((*y) * (*y)));
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gradY->applyTransform(transform::Neg, nullptr, nullptr);
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//epsNext->applyLambda(lambdaS, gradX);
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epsNext->applyScalarArr(scalar::Divide, y, gradX, nullptr);
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} else {
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// broadcast case
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auto preX = *epsNext / *y;
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NDArray negX(*x);
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x->applyTransform(transform::Neg, &negX);
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auto preY = *epsNext * negX / ((*y) * (*y));
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auto axisX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), epsNext->shapeInfo());
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auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());
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if (axisX.size() > 0) {
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auto sum = preX.reduceAlongDimension(reduce::Sum, axisX);
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gradX->assign(sum);
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delete sum;
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} else
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gradX->assign(preX);
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if (axisY.size() > 0) {
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auto sum = preY.reduceAlongDimension(reduce::Sum, axisY);
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gradY->assign(sum);
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delete sum;
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} else
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gradY->assign(preY);
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}
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return Status::OK();
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}
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DECLARE_SHAPE_FN(divide_bp) {
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auto x = inputShape->at(0);
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auto y = inputShape->at(1);
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auto e = inputShape->at(2);
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// eps always has shape of x
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// grad always has shape of y
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Nd4jLong *shapeE;
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Nd4jLong *shapeG;
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COPY_SHAPE(x, shapeE);
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COPY_SHAPE(y, shapeG);
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return SHAPELIST(CONSTANT(shapeE), CONSTANT(shapeG));
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}
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}
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}
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#endif |