318 lines
15 KiB
C++
318 lines
15 KiB
C++
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/*******************************************************************************
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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 Yurii Shyrma (iuriish@yahoo.com), created on 23.11.2017
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_huber_loss)
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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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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(huber_loss, 3, 1, false, 1, 1) {
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auto predictions = INPUT_VARIABLE(0);
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auto weights = INPUT_VARIABLE(1);
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auto labels = INPUT_VARIABLE(2);
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auto output = OUTPUT_VARIABLE(0);
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int reductionMode = INT_ARG(0); // 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
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// FIXME: double?
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double delta = T_ARG(0);
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// input validation
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REQUIRE_TRUE(labels->isSameShape(predictions), 0, "HUBER_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(predictions).c_str());
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// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
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REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == labels->rankOf(), 0, "HUBER_LOSS OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", weights->rankOf(), labels->rankOf());
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// check whether broadcast operation is possible for weights array
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REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *labels), 0, "HUBER_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
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// only 4 possible reduction modes exist
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REQUIRE_TRUE(reductionMode==0 || reductionMode==1 || reductionMode==2 || reductionMode==3, 0, "HUBER_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);
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// perform weights broadcasting/tile to predictions if needed
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auto weightsBroad = weights;
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if(!weights->isScalar() && !weights->isSameShape(predictions))
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weightsBroad = new NDArray(weights->tileToShape(predictions->getShapeInfo()));
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auto error = *predictions - *labels;
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error.applyTransform(transform::Abs);
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NDArray quadratic(error.getShapeInfo(), block.getWorkspace());
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error.applyScalar(scalar::MinPairwise, delta, &quadratic);
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NDArray E = quadratic * quadratic * 0.5f + (error - quadratic)*delta;
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// multiply E on weights
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E *= *weightsBroad;
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switch (reductionMode) {
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case 0: { // 0 - "none", un-reduced weighted losses with the same shape as labels.
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output->assign(E);
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break;
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}
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case 1: { // 1 - "weighted_sum", output is scalar and equal to sum of all elements of E array
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E.reduceNumber(reduce::Sum, *output);
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break;
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}
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case 2: { // 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of all elements of weightsBroad array
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NDArray sum;
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if (weights->isScalar())
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sum = *weights * E.lengthOf();
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else
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sum = weightsBroad->reduceNumber(reduce::Sum);
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if (sum.e<double>(0) == 0.)
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*output = 0.;
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else
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output->assign(E.reduceNumber(reduce::Sum) / sum);
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break;
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}
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case 3: { // 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E array divided by number of non-zero weights
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Nd4jLong numOfNonZeroWeights = 0;
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if(weights->isScalar()) {
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if(weights->e<double>(0) != 0.)
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numOfNonZeroWeights = E.lengthOf();
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}
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else {
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numOfNonZeroWeights = weightsBroad->reduceNumber(reduce::CountNonZero).e<Nd4jLong>(0);
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}
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if (numOfNonZeroWeights == 0)
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(*output) = 0.;
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else
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output->assign(E.reduceNumber(reduce::Sum) / double(numOfNonZeroWeights));
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break;
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}
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}
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if(weightsBroad != weights)
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delete weightsBroad;
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_TYPES(huber_loss) {
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getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_SHAPE_FN(huber_loss) {
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auto predictionsShapeInfo = inputShape->at(0);
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auto weightsShapeInfo = inputShape->at(1);
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auto labelsShapeInfo = inputShape->at(2);
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// labels and predictions must have the same shapes
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REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0, "HUBER_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(predictionsShapeInfo).c_str());
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// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
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REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(labelsShapeInfo), 0, "HUBER_LOSS OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(labelsShapeInfo));
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// check whether broadcast operation is possible for weights array
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REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0, "HUBER_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(labelsShapeInfo).c_str());
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DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(predictionsShapeInfo));
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Nd4jLong* outShapeInfo = nullptr;
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if(INT_ARG(0) != 0) // in this case output is scalar
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outShapeInfo = ConstantShapeHelper::getInstance()->scalarShapeInfo(outType);
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else // in this case output has the same shape as labels and predictions
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outShapeInfo = ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(outType, shape::order(labelsShapeInfo), shape::shapeOf(labelsShapeInfo), shape::rank(labelsShapeInfo)));
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return SHAPELIST(outShapeInfo);
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(huber_loss_grad, 3, 3, false, 1, 1) {
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auto predictions = INPUT_VARIABLE(0);
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auto weights = INPUT_VARIABLE(1);
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auto labels = INPUT_VARIABLE(2);
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auto dLdp = OUTPUT_VARIABLE(0); // dL/dpredictions
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auto dLdw = OUTPUT_VARIABLE(1); // dL/dweights
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auto dLdl = OUTPUT_VARIABLE(2); // dL/dlabels
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auto delta = T_ARG(0);
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int reductionMode = INT_ARG(0); // 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
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// take into account Alex's proposition to treat "none" the same as "weighted_sum" mode when calculating gradients
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if(reductionMode == 0)
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reductionMode = 1;
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// inputs validation
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REQUIRE_TRUE(labels->isSameShape(predictions), 0, "HUBER_LOSS_GRAD OP: labels and predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(predictions).c_str());
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// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
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REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == labels->rankOf(), 0, "HUBER_LOSS_GRAD OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", weights->rankOf(), labels->rankOf());
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// check whether broadcast operation is possible for weights array
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REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *labels), 0, "HUBER_LOSS_GRAD OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
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// only 4 possible reduction modes exist
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REQUIRE_TRUE(reductionMode==0 || reductionMode==1 || reductionMode==2 || reductionMode==3, 0, "HUBER_LOSS_GRAD OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);
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// perform weights broadcasting/tile to labels if needed
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auto weightsBroad = weights;
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if(!weights->isScalar() && !weights->isSameShape(predictions))
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weightsBroad = new NDArray(weights->tileToShape(predictions->getShapeInfo()));
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NDArray diff = *predictions - *labels;
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NDArray absDiff(diff);
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absDiff.applyTransform(transform::Abs);
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NDArray quadratic(absDiff);
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absDiff.applyScalar(scalar::MinPairwise, delta, &quadratic);
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NDArray E = quadratic * quadratic * 0.5f + (absDiff - quadratic)*delta;
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NDArray lteMask(diff.getShapeInfo(), BOOL, true, block.launchContext());
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absDiff.applyScalar(scalar::LessThanOrEqual, delta, <eMask);
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NDArray gtMask(diff.getShapeInfo(), BOOL, true, block.launchContext());
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absDiff.applyScalar(scalar::GreaterThan, delta, >Mask);
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NDArray signDiff(diff);
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diff.applyTransform(transform::Sign, &signDiff);
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auto gtMaskFloat = *gtMask.cast(diff.dataType());
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auto lteMaskFloat = *lteMask.cast(diff.dataType());
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dLdp->assign( lteMaskFloat * diff + gtMaskFloat * delta * signDiff);
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dLdl->assign(-lteMaskFloat * diff - gtMaskFloat * delta * signDiff);
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switch (reductionMode) {
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case 1: { // 1 - "none" and "weighted_sum", output is scalar and equal to sum of all elements of E array
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*dLdp *= *weightsBroad;
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*dLdl *= *weightsBroad;
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if(weights->isScalar())
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dLdw->assign(E.reduceNumber(reduce::Sum));
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else if(weights != weightsBroad) {
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std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->getShapeInfo(), weightsBroad->getShapeInfo());
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E.reduceAlongDimension(reduce::Sum, dLdw, axesToReduceAlong, true, false, false);
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}
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else
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dLdw->assign(E);
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break;
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}
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case 2: { // 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of all elements of weightsBroad array
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NDArray sum;
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if (weights->isScalar())
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sum = (*weights) * E.lengthOf();
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else
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sum = weightsBroad->reduceNumber(reduce::Sum);
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if (sum.e<double>(0) == 0.) {
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*dLdp = 0.;
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*dLdl = 0.;
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*dLdw = 0.;
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}
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else {
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*dLdp *= *weightsBroad / sum;
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*dLdl *= *weightsBroad / sum;
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if(weights->isScalar())
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*dLdw = 0.;
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else if(weights != weightsBroad) {
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std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->getShapeInfo(), weightsBroad->getShapeInfo());
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((E * sum - (E * *weightsBroad).reduceNumber(reduce::Sum)) / (sum*sum)).reduceAlongDimension(reduce::Sum, dLdw, axesToReduceAlong, true, false, false);
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}
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else
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dLdw->assign((E * sum - (E * *weightsBroad).reduceNumber(reduce::Sum)) / (sum*sum));
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}
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break;
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}
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case 3: { // 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E array divided by number of non-zero weights
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Nd4jLong numOfNonZeroWeights = 0;
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if(weights->isScalar()) {
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if(weights->e<double>(0) != 0.)
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numOfNonZeroWeights = E.lengthOf();
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}
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else
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numOfNonZeroWeights = weightsBroad->reduceNumber(reduce::CountNonZero).e<Nd4jLong>(0);
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if (numOfNonZeroWeights == 0) {
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*dLdp = 0.;
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*dLdl = 0.;
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*dLdw = 0.;
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}
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else {
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auto numOfNonZeroWeightsScalar = NDArrayFactory::create(dLdw->dataType(), numOfNonZeroWeights, block.launchContext());
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if(weights->isScalar())
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dLdw->assign(E.reduceNumber(reduce::Sum) / double(numOfNonZeroWeights));
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else if(weights != weightsBroad) {
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std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->getShapeInfo(), weightsBroad->getShapeInfo());
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E.reduceAlongDimension(reduce::Sum, dLdw, axesToReduceAlong, true, false, false);
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*dLdw /= numOfNonZeroWeightsScalar;
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}
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else
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dLdw->assign(E / numOfNonZeroWeightsScalar);
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NDArray temp = *weightsBroad / numOfNonZeroWeightsScalar;
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*dLdp *= temp;
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*dLdl *= temp;
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}
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break;
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}
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}
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if(weightsBroad != weights)
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delete weightsBroad;
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return Status::OK();
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}
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DECLARE_TYPES(huber_loss_grad) {
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getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(huber_loss_grad) {
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auto predictionsShapeInfo = inputShape->at(0);
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auto weightsShapeInfo = inputShape->at(1);
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auto labelsShapeInfo = inputShape->at(2);
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// labels and predictions must have the same shapes
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REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0, "HUBER_LOSS_GRAD OP: labels and predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(predictionsShapeInfo).c_str());
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// weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
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REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(labelsShapeInfo), 0, "HUBER_LOSS_GRAD OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(labelsShapeInfo));
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// check whether broadcast operation is possible for weights array
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REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0, "HUBER_LOSS_GRAD OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(labelsShapeInfo).c_str());
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DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(predictionsShapeInfo));
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Nd4jLong *dLdpShapeInfo = ShapeBuilders::copyShapeInfoAndType(predictionsShapeInfo, outType, false, block.getWorkspace());
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Nd4jLong *dLdwShapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, outType, false, block.getWorkspace());
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Nd4jLong *dLdlShapeInfo = ShapeBuilders::copyShapeInfoAndType(labelsShapeInfo, outType, false, block.getWorkspace());
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return SHAPELIST(dLdpShapeInfo, dLdwShapeInfo, dLdlShapeInfo);
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||
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}
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||
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||
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|
|
||
|
|
}
|
||
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|
}
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||
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|
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||
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#endif
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