wehub-resource-sync
427 lines
18 KiB
C++
427 lines
18 KiB
C++
/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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 <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_log_poisson_loss)
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#include <ops/declarable/CustomOperations.h>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(log_poisson_loss, 3, 1, true, 0, 1) {
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auto log_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 =
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INT_ARG(0); // 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
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bool computeFullLoss = false;
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if (block.numI() > 1) computeFullLoss = INT_ARG(1) != 0;
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// inputs validation
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REQUIRE_TRUE(labels->isSameShape(log_predictions), 0,
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"LOG_POISSON_LOSS OP: labels and log_predictions arrays must have the same shapes, but got %s and %s "
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"correspondingly !",
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ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(log_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,
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"LOG_POISSON_LOSS OP: weights array should be scalar or have the same rank as labels array, but got %i "
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"and %i correspondingly!",
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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,
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"LOG_POISSON_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s "
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"and labels = %s instead!",
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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,
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"LOG_POISSON_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got "
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"%i instead!",
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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(log_predictions))
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weightsBroad = new NDArray(weights->tileToShape(log_predictions->shapeInfo()));
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NDArray E(labels->shapeInfo(), block.getWorkspace());
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if (computeFullLoss)
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labels->applyPairwiseTransform(pairwise::LogPoissonLossFull, log_predictions, &E);
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else
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labels->applyPairwiseTransform(pairwise::LogPoissonLoss, log_predictions, &E);
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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
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// all elements of weightsBroad array
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NDArray* sum = nullptr;
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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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}
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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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NDArray* sumResult = E.reduceNumber(reduce::Sum);
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NDArray* assign = (*sumResult) / (*sum);
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delete sumResult;
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output->assign(assign);
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delete assign;
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}
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delete 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
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// array divided by number of non-zero weights
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LongType numOfNonZeroWeights = 0;
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if (weights->isScalar()) {
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if (weights->e<double>(0) != 0.) numOfNonZeroWeights = E.lengthOf();
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} else {
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NDArray* countResult = weightsBroad->reduceNumber(reduce::CountNonZero);
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numOfNonZeroWeights = countResult->e<LongType>(0);
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delete countResult;
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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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NDArray* sumResult = E.reduceNumber(reduce::Sum);
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NDArray* assign = (*sumResult) / double(numOfNonZeroWeights);
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delete sumResult;
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output->assign(assign);
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delete assign;
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}
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break;
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}
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}
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if (weightsBroad != weights) delete weightsBroad;
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return Status::OK;
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_TYPES(log_poisson_loss) {
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getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_SHAPE_FN(log_poisson_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,
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"LOG_POISSON_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and %s "
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"correspondingly !",
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ShapeUtils::shapeAsString(labelsShapeInfo).c_str(),
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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,
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"LOG_POISSON_LOSS OP: weights array should be scalar or have the same rank as labels array, but got %i "
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"and %i correspondingly!",
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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(
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shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0,
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"LOG_POISSON_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and "
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"labels = %s instead!",
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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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LongType* 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().bufferForShapeInfo(outType, shape::order(labelsShapeInfo),
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shape::rank(labelsShapeInfo),
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shape::shapeOf(labelsShapeInfo))->primary();
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}
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return SHAPELIST(outShapeInfo);
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(log_poisson_loss_grad, 3, 3, false, 0, 1) {
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auto log_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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int reductionMode =
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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) reductionMode = 1;
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bool computeFullLoss = false;
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if (block.numI() > 1) computeFullLoss = INT_ARG(1) != 0;
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// inputs validation
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REQUIRE_TRUE(labels->isSameShape(log_predictions), 0,
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"LOG_POISSON_LOSS_GRAD OP: labels and log_predictions arrays must have the same shapes, but got %s and "
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"%s correspondingly !",
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ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(log_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,
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"LOG_POISSON_LOSS_GRAD OP: weights array should be scalar or have the same rank as labels array, but "
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"got %i and %i correspondingly!",
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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,
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"LOG_POISSON_LOSS_GRAD OP: shapes of weights and labels arrays should be broadcastable, but got weights "
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"= %s and labels = %s instead!",
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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,
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"LOG_POISSON_LOSS_GRAD OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but "
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"got %i instead!",
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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(log_predictions))
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weightsBroad = new NDArray(weights->tileToShape(log_predictions->shapeInfo()));
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NDArray E(labels->shapeInfo(), block.getWorkspace());
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if (computeFullLoss) {
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labels->applyPairwiseTransform(pairwise::LogPoissonLossFull, log_predictions, &E);
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NDArray rDiv(labels->shapeInfo(), block.getWorkspace());
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labels->applyScalar(scalar::ReverseDivide, 0.5f, &rDiv);
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// For dLdl - first case
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NDArray* logLabels = labels->transform(transform::Log);
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NDArray negLogPredictions = -(*log_predictions); // unary negation returns value
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NDArray* temp1 = rDiv + (*logLabels);
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delete logLabels;
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NDArray* dLdlTemp1 = (*temp1) + negLogPredictions;
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delete temp1;
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dLdl->assign(dLdlTemp1);
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delete dLdlTemp1;
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} else {
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labels->applyPairwiseTransform(pairwise::LogPoissonLoss, log_predictions, &E);
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// For dLdl - second case
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NDArray dLdlTemp2 = -(*log_predictions); // unary negation returns value
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dLdl->assign(&dLdlTemp2);
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}
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// For dLdp
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NDArray* expResult = log_predictions->transform(transform::Exp);
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NDArray* dLdpTemp = (*expResult) - (*labels);
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delete expResult;
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dLdp->assign(dLdpTemp);
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delete dLdpTemp;
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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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NDArray* assign = E.reduceNumber(reduce::Sum);
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dLdw->assign(assign);
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delete assign;
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} else if (weights != weightsBroad) {
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std::vector<LongType> axesToReduceAlong =
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ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
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E.reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
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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
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// all elements of weightsBroad array
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NDArray* sum = nullptr;
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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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}
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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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} else {
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NDArray* weightsBroadDivSum = (*weightsBroad) / (*sum);
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*dLdp *= *weightsBroadDivSum;
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*dLdl *= *weightsBroadDivSum;
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delete weightsBroadDivSum;
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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<LongType> axesToReduceAlong =
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ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
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NDArray* eMulWeightsBroad = E * (*weightsBroad);
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NDArray* sumReduced = eMulWeightsBroad->reduceNumber(reduce::Sum);
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delete eMulWeightsBroad;
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NDArray* eMulSum = E * (*sum);
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NDArray* numerator = (*eMulSum) - (*sumReduced);
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delete eMulSum;
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delete sumReduced;
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NDArray* sumSquared = (*sum) * (*sum);
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NDArray* result = (*numerator) / (*sumSquared);
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delete numerator;
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delete sumSquared;
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result->reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
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delete result;
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} else {
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NDArray* eMulWeightsBroad = E * (*weightsBroad);
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NDArray* sumReduced = eMulWeightsBroad->reduceNumber(reduce::Sum);
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delete eMulWeightsBroad;
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NDArray* eMulSum = E * (*sum);
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NDArray* numerator = (*eMulSum) - (*sumReduced);
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delete eMulSum;
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delete sumReduced;
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NDArray* sumSquared = (*sum) * (*sum);
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NDArray* assign = (*numerator) / (*sumSquared);
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delete numerator;
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delete sumSquared;
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dLdw->assign(assign);
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delete assign;
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}
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}
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delete 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
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// array divided by number of non-zero weights
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LongType numOfNonZeroWeights = 0;
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if (weights->isScalar()) {
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if (weights->e<double>(0) != 0.) numOfNonZeroWeights = E.lengthOf();
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} else {
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NDArray* countResult = weightsBroad->reduceNumber(reduce::CountNonZero);
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numOfNonZeroWeights = countResult->e<LongType>(0);
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delete countResult;
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}
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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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} else {
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auto* numOfNonZeroWeightsScalar =
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NDArrayFactory::create(dLdw->dataType(), numOfNonZeroWeights, block.launchContext());
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if (weights->isScalar()) {
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NDArray* sumResult = E.reduceNumber(reduce::Sum);
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NDArray* assign = (*sumResult) / double(numOfNonZeroWeights);
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delete sumResult;
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dLdw->assign(assign);
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delete assign;
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} else if (weights != weightsBroad) {
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std::vector<LongType> axesToReduceAlong =
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ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
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E.reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
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*dLdw /= *numOfNonZeroWeightsScalar;
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} else {
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NDArray* assign = E / (*numOfNonZeroWeightsScalar);
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dLdw->assign(assign);
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delete assign;
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}
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NDArray* temp = (*weightsBroad) / (*numOfNonZeroWeightsScalar);
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*dLdp *= *temp;
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*dLdl *= *temp;
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delete temp;
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delete numOfNonZeroWeightsScalar;
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}
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break;
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}
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}
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if (weightsBroad != weights) delete weightsBroad;
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return Status::OK;
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}
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DECLARE_TYPES(log_poisson_loss_grad) {
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getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(log_poisson_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,
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"LOG_POISSON_LOSS_GRAD OP: labels and predictions arrays must have the same shapes, but got %s and %s "
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"correspondingly !",
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ShapeUtils::shapeAsString(labelsShapeInfo).c_str(),
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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,
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"LOG_POISSON_LOSS_GRAD OP: weights array should be scalar or have the same rank as labels array, but "
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"got %i and %i correspondingly!",
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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(
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shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0,
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"LOG_POISSON_LOSS_GRAD OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and "
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"labels = %s instead!",
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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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auto dLdpShapeInfo = ShapeBuilders::copyShapeInfoAndType(predictionsShapeInfo, outType, false, block.getWorkspace());
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auto dLdwShapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, outType, false, block.getWorkspace());
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auto 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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} // namespace ops
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} // namespace sd
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#endif
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