398 lines
19 KiB
C++
398 lines
19 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 Yurii Shyrma (iuriish@yahoo.com), created on 25.11.2017.
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_softmax_cross_entropy_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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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(softmax_cross_entropy_loss, 3, 1, false, 1, 1) {
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auto logits = 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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double labelsSmoothing = T_ARG(0);
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// input validation
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REQUIRE_TRUE(labels->isSameShape(logits), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: labels and logits arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(logits).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, "SOFTMAX_CROSS_ENTROPY_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);
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// smoothing is possible for rank of logits/labels > 1
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REQUIRE_TRUE(labels->rankOf() > 1 || (labels->rankOf() == 1 && labelsSmoothing == 0.), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: smoothing is not possible when rank of labels/ logits = 1 !");
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if(!output->isScalar()) {
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// weights array can be single scalar or has the same shape as output, and must be broadcastable to output shape
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REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == output->rankOf(), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: weights array should be scalar or have the same rank as output array, but got %i and %i correspondingly!", weights->rankOf(), output->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, *output), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: shapes of weights and output arrays should be broadcastable, but got weights = %s and output = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
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}
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// If label_smoothing is nonzero, smooth the labels towards 1/num_classes: new_onehot_labels = onehot_labels * (1 - label_smoothing) + label_smoothing / num_classes
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// num_classes = labels->sizeAt(1)
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NDArray* cLabels = new NDArray(labels->cast(weights->dataType()));
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NDArray* newLabels = cLabels;
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if(labelsSmoothing != 0.) {
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newLabels = new NDArray(cLabels);
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newLabels->assign((1.f - labelsSmoothing) * *cLabels + labelsSmoothing / cLabels->sizeAt(1));
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}
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// main formula: result = - sum_i(lables_i * log(softmax_i)) - sum over last dimension
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// softmax_i = exp(logits_i) / sum_j(exp(logits_j))
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// so result = sum_i( lables_i * (log(sum_j(exp(logits_j))) - logits_i) )
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// for numerical stability we use shifted logits (one can approve this using simple math):
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// softmax_i = exp(logits_i - maxLogit) / sum_j(exp(logits_j - maxLogit))
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// maxLogit is max among logits_i
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std::vector<int> dimensions = {-1};
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NDArray shiftedLogits = *logits - logits->reduceAlongDimension(reduce::Max, dimensions, true);
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NDArray logSumExp = shiftedLogits.transform(transform::Exp).reduceAlongDimension(reduce::Sum, dimensions, true).transform(transform::Log);
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NDArray E = (*newLabels * (logSumExp - shiftedLogits)).reduceAlongDimension(reduce::Sum, dimensions);
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// perform weights broadcasting/tile to E if it is necessary
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auto weightsBroad = weights;
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if(!weights->isScalar() && !weights->isSameShape(&E)) {
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if(E.rankOf() == 1 && weights->isVector() && weights->rankOf() > 1)
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weightsBroad = new NDArray(weights->reshape(weights->ordering(), {weights->lengthOf()}));
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else
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weightsBroad = new NDArray(weights->tileToShape(E.shapeInfo()));
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}
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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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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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double sum;
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if (weights->isScalar())
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sum = weights->e<double>(0) * E.lengthOf();
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else
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sum = weightsBroad->reduceNumber(reduce::Sum).e<double>(0);
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if (sum == 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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if(newLabels != cLabels)
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delete newLabels;
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delete cLabels;
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_TYPES(softmax_cross_entropy_loss) {
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getOpDescriptor()->setAllowedInputTypes(0, {ALL_FLOATS})
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedInputTypes(2, {ALL_FLOATS, ALL_INTS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_SHAPE_FN(softmax_cross_entropy_loss) {
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auto logitsShapeInfo = 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 logits must have the same shapes
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REQUIRE_TRUE(shape::shapeEquals(logitsShapeInfo, labelsShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: labels and logits arrays must have the same shapes, but got %s and %s correspondingly!", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
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DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
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Nd4jLong const* 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 shape as labels and logits minus last dimension
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std::vector<int> dimensions = {-1};
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outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(logitsShapeInfo), dimensions, logitsShapeInfo, false, true, block.getWorkspace());
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// weights array can be single scalar or has the same rank as output, and must be broadcastable to output
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REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(outShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: weights array should be scalar or have the same rank as output array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(outShapeInfo));
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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, outShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: shapes of weights and output arrays should be broadcastable, but got weights = %s and output = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(outShapeInfo).c_str());
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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(softmax_cross_entropy_loss_grad, 3, 3, false, 1, 1) {
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auto logits = 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/dlogits
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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 labelsSmoothing = 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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std::vector<int> dimensions = {-1};
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// input validation
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REQUIRE_TRUE(labels->isSameShape(logits), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: labels and logits arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(logits).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, "SOFTMAX_CROSS_ENTROPY_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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auto lossShapeInfo = ShapeUtils::evalReduceShapeInfo(logits->ordering(), dimensions, logits->shapeInfo(), false, false, block.getWorkspace());
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// weights array can be single scalar or has the same shape as loss, and must be broadcastable to loss shape
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REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == shape::rank(lossShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: weights array should be scalar or have the same rank as loss array, but got %i and %i correspondingly!", weights->rankOf(), shape::rank(lossShapeInfo));
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// check whether broadcast operation is possible for weights array
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REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(weights->shapeInfo(), lossShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: shapes of weights and loss arrays should be broadcastable, but got weights = %s and loss = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(lossShapeInfo).c_str());
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// smoothing is possible for rank of logits/labels > 1
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REQUIRE_TRUE(labels->rankOf() > 1 || (labels->rankOf() == 1 && labelsSmoothing == 0.), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: smoothing is not possible when rank of labels/ logits = 1 !");
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// If label_smoothing is nonzero, smooth the labels towards 1/num_classes: new_onehot_labels = onehot_labels * (1 - label_smoothing) + label_smoothing / num_classes
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// num_classes = labels->sizeAt(1)
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NDArray* cLabels = new NDArray(labels->cast(weights->dataType()));
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NDArray* newLabels = cLabels;
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if(labelsSmoothing != 0.) {
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newLabels = new NDArray(labels->shapeInfo(), dLdl->dataType(), false, block.launchContext());
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newLabels->assign((1.f - labelsSmoothing) * *cLabels + labelsSmoothing / cLabels->sizeAt(1));
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}
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NDArray softmax = (*logits - logits->reduceAlongDimension(reduce::Max, dimensions, true)).transform(transform::Exp);
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softmax /= softmax.reduceAlongDimension(reduce::Sum, dimensions, true);
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// dEdp = softmax * sum_i(lables_i) - labels
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dLdp->assign(softmax * newLabels->reduceAlongDimension(reduce::Sum, dimensions, true) - *newLabels);
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// dEdl = -log(softmax)
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dLdl->assign(-softmax.transform(transform::Log)* (1.f - labelsSmoothing));
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NDArray shiftedLogits = *logits - logits->reduceAlongDimension(reduce::Max, dimensions, true);
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NDArray logSumExp = shiftedLogits.transform(transform::Exp).reduceAlongDimension(reduce::Sum, dimensions, true).transform(transform::Log);
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NDArray E = (*newLabels * (logSumExp - shiftedLogits)).reduceAlongDimension(reduce::Sum, dimensions);
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// perform weights broadcasting/tile to E if it is necessary
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auto weightsBroad = weights;
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if(!weights->isScalar() && !weights->isSameShape(&E))
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weightsBroad = new NDArray(weights->tileToShape(E.shapeInfo()));
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dimensions = ShapeUtils::evalDimsToExclude(dLdp->rankOf(), dimensions);
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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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if(weights->isScalar() || weights->lengthOf() == 1) {
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dLdw->assign(E.reduceNumber(reduce::Sum));
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*dLdp *= *weights;
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*dLdl *= *weights;
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}
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else {
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dLdp->applyBroadcast(sd::broadcast::Multiply, dimensions, *weightsBroad, *dLdp);
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dLdl->applyBroadcast(sd::broadcast::Multiply, dimensions, *weightsBroad, *dLdl);
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if(weights != weightsBroad) {
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std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
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E.reduceAlongDimension(reduce::Sum, *dLdw, axesToReduceAlong, true);
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}
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else
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dLdw->assign(E);
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}
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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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if(weights->isScalar() || weights->lengthOf() == 1) {
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NDArray temp = *weights / sum;
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*dLdp *= temp;
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*dLdl *= temp;
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*dLdw = 0.;
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}
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else {
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NDArray temp = *weightsBroad / sum;
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dLdp->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdp);
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dLdl->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdl);
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if(weights != weightsBroad) {
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std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
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((E * sum - (E * *weightsBroad).reduceNumber(reduce::Sum)) / (sum*sum)).reduceAlongDimension(reduce::Sum, *dLdw, axesToReduceAlong, true);
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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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}
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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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if(weights->isScalar() || weights->lengthOf() == 1) {
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NDArray temp = *weights / numOfNonZeroWeights;
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*dLdp *= temp;
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*dLdl *= temp;
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dLdw->assign(E.reduceNumber(reduce::Sum) / numOfNonZeroWeights);
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}
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else {
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NDArray temp = *weightsBroad / numOfNonZeroWeights;
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dLdp->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdp);
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dLdl->applyBroadcast(sd::broadcast::Multiply, dimensions, temp, *dLdl);
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if(weights != weightsBroad) {
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std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
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E.reduceAlongDimension(reduce::Sum, *dLdw, axesToReduceAlong, true);
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*dLdw /= numOfNonZeroWeights;
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}
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else
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dLdw->assign(E / numOfNonZeroWeights);
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}
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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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if(newLabels != cLabels)
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delete newLabels;
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delete cLabels;
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_TYPES(softmax_cross_entropy_loss_grad) {
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getOpDescriptor()->setAllowedInputTypes(0, {ALL_FLOATS})
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedInputTypes(2, {ALL_FLOATS, ALL_INTS})
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->setAllowedInputTypes(3, {ALL_FLOATS})
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->setAllowedInputTypes(4, {ALL_FLOATS})
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->setAllowedInputTypes(5, {ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_SHAPE_FN(softmax_cross_entropy_loss_grad) {
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auto logitsShapeInfo = 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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std::vector<int> dimensions = {-1};
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// labels and logits must have the same shapes
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REQUIRE_TRUE(shape::shapeEquals(logitsShapeInfo, labelsShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: labels and logits arrays must have the same shapes, but got %s and %s correspondingly!", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
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auto lossShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(logitsShapeInfo), dimensions, logitsShapeInfo, false, false, block.getWorkspace());
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// weights array can be single scalar or has the same rank as loss, and must be broadcastable to loss
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REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(lossShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: weights array should be scalar or have the same rank as loss array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(lossShapeInfo));
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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, lossShapeInfo), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_GRAD OP: shapes of weights and loss arrays should be broadcastable, but got weights = %s and loss = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(lossShapeInfo).c_str());
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auto outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
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auto dLdpShapeInfo = ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(outType, shape::order(logitsShapeInfo), shape::shapeOf(logitsShapeInfo), shape::rank(logitsShapeInfo)));
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auto dLdwShapeInfo = ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(outType, shape::order(weightsShapeInfo), shape::shapeOf(weightsShapeInfo), shape::rank(weightsShapeInfo)));
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auto dLdlShapeInfo = ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(outType, shape::order(labelsShapeInfo), shape::shapeOf(labelsShapeInfo), shape::rank(labelsShapeInfo)));
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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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#endif |