130 lines
4.5 KiB
C++
130 lines
4.5 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_reversedivide)
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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(reversedivide, 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(!x->isB(), 0, "REVERSEDIVIDE OP: you can't divide by bool array!");
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x->applyTrueBroadcast(BROADCAST(ReverseDivide), y, z, true);
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return Status::OK();
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}
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DECLARE_SYN(RDiv, reversedivide);
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DECLARE_TYPES(reversedivide) {
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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(reversedivide_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(reversedivide_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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if (x->isSameShape(y)) {
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// PWT case case
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// X gradient
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//epsNext->applyTriplewiseLambda(x, y, lambdaX, gradX);
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gradX->assign((*epsNext) * (*y) / ((*x) * (*x)));
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gradX->applyTransform(transform::Neg, nullptr, nullptr);
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// Y gradient
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//epsNext->applyPairwiseLambda(x, lambdaY, gradY);
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gradY->assign((*epsNext) / (*x));
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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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gradY->assign(tmp / tmpX);
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gradX->assign((*epsNext) * (*y) / ((*x) * (*x)));
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gradX->applyTransform(transform::Neg, nullptr, nullptr);
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} else {
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// broadcast case
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auto preY = (*epsNext) / (*x);
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auto preX = *epsNext * (*y) / ((*x) * (*x));
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preX.applyTransform(transform::Neg, nullptr, nullptr);
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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(reversedivide_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 |