145 lines
6.9 KiB
C++
145 lines
6.9 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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#ifndef LIBND4J_BROADCAST_HELPER_H
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#define LIBND4J_BROADCAST_HELPER_H
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#include <NDArray.h>
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#include <helpers/ShapeUtils.h>
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#include <ops/BroadcastOpsTuple.h>
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#include <ops/BroadcastBoolOpsTuple.h>
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#include <NDArrayFactory.h>
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namespace nd4j {
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namespace ops {
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class BroadcastHelper {
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public:
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static FORCEINLINE NDArray* broadcastApply(nd4j::BroadcastOpsTuple op, NDArray* x, NDArray* y, NDArray* z, ExtraArguments *extraArgs = nullptr) {
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if(x->isEmpty() || y->isEmpty()) {
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if(!z->isEmpty())
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throw std::runtime_error("BroadcastHelper::broadcastApply: when some of input arrays (or both) is empty, output array must be empty as well !");
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return z;
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}
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std::unique_ptr<NDArray> ptr;
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if (!Environment::getInstance()->isExperimentalBuild()) {
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if (y->dataType() != x->dataType()) {
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y = new NDArray(y->cast(x->dataType()));
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std::unique_ptr<NDArray> ptr2(y);
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ptr.swap(ptr2);
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}
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}
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if (!x->isScalar() && !y->isScalar() && x->isSameShape(y)) {
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x->applyPairwiseTransform(op.p, *y, *z);
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} else if (!x->isScalar() && y->isScalar()) {
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x->applyScalarArr(op.s, const_cast<const NDArray&>(*y), *z);
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} else if (x->isScalar() && !y->isScalar()) {
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if (z->isSameShape(y)) {
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if (op.s == scalar::Add || op.s == scalar::Multiply ) {
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y->applyScalarArr(op.s, *x, *z);
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} else if (op.s == scalar::SquaredSubtract) {
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y->applyScalarArr(scalar::SquaredReverseSubtract, *x, *z);
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} else if (op.s == scalar::Subtract) {
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y->applyScalarArr(scalar::ReverseSubtract, *x, *z);
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} else if (op.s == scalar::Divide) {
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y->applyScalarArr(scalar::ReverseDivide, *x, *z);
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} else if (op.s == scalar::Pow) {
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y->applyScalarArr(scalar::ReversePow, *x, *z);
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} else if (op.s == scalar::ReverseSubtract) {
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y->applyScalarArr(scalar::Subtract, *x, *z);
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} else if (op.s == scalar::ReverseDivide) {
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y->applyScalarArr(scalar::Divide, *x, *z);
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} else if (op.s == scalar::MaxPairwise || op.s == scalar::MinPairwise || op.s == scalar::AMaxPairwise || op.s == scalar::AMinPairwise) {
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y->applyScalarArr(op.s, *x, *z);
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} else if (op.s == scalar::CopyPws) {
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z->assign(y);
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} else {
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z->assign(x);
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z->applyPairwiseTransform(op.p, *y, extraArgs);
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}
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return z;
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} else {
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auto v = y->getShapeAsVector();
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auto tZ = NDArrayFactory::valueOf(v, y, y->ordering());
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tZ->applyPairwiseTransform(op.p, *y, extraArgs);
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return tZ;
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}
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} else if (x->isScalar() && y->isScalar()) { // x->isScalar() && y->isScalar()
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x->applyScalarArr(op.s, const_cast<const NDArray&>(*y), *z);
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} else if (ShapeUtils::areShapesBroadcastable(*x, *y)) {
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x->applyTrueBroadcast(op, *y, *z, true, extraArgs);
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return z;
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} else {
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auto sx = ShapeUtils::shapeAsString(x);
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auto sy = ShapeUtils::shapeAsString(y);
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nd4j_printf("Broadcast: shapes should be equal, or broadcastable. But got %s vs %s instead\n", sx.c_str(), sy.c_str());
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return nullptr;
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}
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return z;
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}
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static FORCEINLINE NDArray* broadcastApply(nd4j::BroadcastBoolOpsTuple op, NDArray* x, NDArray* y, NDArray* z, ExtraArguments *extraArgs = nullptr) {
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if(x->isEmpty() || y->isEmpty()) {
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if(!z->isEmpty())
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throw std::runtime_error("BroadcastHelper::broadcastApply: when some of input arrays (or both) is empty, output array must be empty as well !");
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return z;
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}
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if (!x->isScalar() && !y->isScalar() && x->isSameShape(y)) {
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x->applyPairwiseTransform(op.p, *y, *z);
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} else if (ShapeUtils::areShapesBroadcastable(*x, *y)) {
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x->applyTrueBroadcast(op, *y, *z, true, extraArgs);
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return z;
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} else if (!x->isScalar() && y->isScalar()) {
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x->applyScalarArr(op.s, const_cast<const NDArray&>(*y), *z);
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} else if (x->isScalar() && !y->isScalar()) {
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if (z->isSameShape(y)) {
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//z->assign(x);
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x->applyPairwiseTransform(op.p, *y, *z, extraArgs);
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return z;
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} else {
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auto v = y->getShapeAsVector();
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auto tZ = NDArrayFactory::valueOf(v, y, y->ordering());
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//tZ->applyPairwiseTransform(op.p, *y, extraArgs);
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return tZ;
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}
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} else if (x->isScalar() && y->isScalar()) { // x->isScalar() && y->isScalar()
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x->applyScalarArr(op.s, const_cast<const NDArray&>(*y), *z);
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} else if (ShapeUtils::areShapesBroadcastable(*x, *y)) {
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x->applyTrueBroadcast(op, *y, *z, true, extraArgs);
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return z;
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} else {
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auto sx = ShapeUtils::shapeAsString(x);
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auto sy = ShapeUtils::shapeAsString(y);
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nd4j_printf("Broadcast: shapes should be equal, or broadcastable. But got %s vs %s instead\n", sx.c_str(), sy.c_str());
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return nullptr;
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}
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return z;
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}
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};
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}
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}
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#endif |