chore: import upstream snapshot with attribution
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wehub-resource-sync
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/* ******************************************************************************
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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 Yurii Shyrma (iuriish@yahoo.com), created on 18.06.2018
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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_with_logits)
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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_with_logits, 2, 1, false, 0, 0) {
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auto logits = INPUT_VARIABLE(0);
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auto labels = INPUT_VARIABLE(1);
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auto output = OUTPUT_VARIABLE(0);
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const int classesDim = block.getIArguments()->size() > 0 ? INT_ARG(0) : logits->rankOf() - 1;
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// input validation
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REQUIRE_TRUE(labels->isSameShape(logits), 0,
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"SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: labels and logits arrays must have the same shapes, but got "
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"%s and %s correspondingly !",
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ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(logits).c_str());
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REQUIRE_TRUE(classesDim < logits->rankOf(), 0,
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"SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: class dimension must be smaller than rank of logits, but "
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"got %i and %i correspondingly !",
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classesDim, logits->rankOf());
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std::vector<LongType> dimension = {classesDim};
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// Compute softmax log
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NDArray* maxAlongDim_ptr = logits->reduceAlongDimension(reduce::Max, &dimension, true);
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NDArray maxAlongDim = *maxAlongDim_ptr;
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delete maxAlongDim_ptr;
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NDArray* shiftedLogits_ptr = (*logits) - maxAlongDim;
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NDArray* logExp_ptr = shiftedLogits_ptr->transform(transform::Exp);
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delete shiftedLogits_ptr;
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NDArray logExp = *logExp_ptr;
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delete logExp_ptr;
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NDArray* sumLogExp_ptr = logExp.reduceAlongDimension(reduce::Sum, &dimension, true);
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NDArray sumLogExp = *sumLogExp_ptr;
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delete sumLogExp_ptr;
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NDArray* softmaxRatio_ptr = logExp / sumLogExp;
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NDArray* logSoftMax_ptr = softmaxRatio_ptr->transform(transform::Log);
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delete softmaxRatio_ptr;
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NDArray logSoftMax = *logSoftMax_ptr;
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delete logSoftMax_ptr;
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// Compute cross entropy: -labels * log(softmax)
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NDArray negLabels = -(*labels); // unary negation returns value
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NDArray* product_ptr = negLabels * logSoftMax;
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product_ptr->reduceAlongDimension(reduce::Sum, output, &dimension);
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delete product_ptr;
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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_with_logits) {
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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(softmax_cross_entropy_loss_with_logits) {
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auto logitsShapeInfo = inputShape->at(0);
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auto labelsShapeInfo = inputShape->at(1);
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const int classesDim = block.getIArguments()->size() > 0 ? INT_ARG(0) : -1;
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std::vector<LongType> dimensions = {classesDim};
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// labels and logits must have the same shapes
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REQUIRE_TRUE(shape::shapeEquals(logitsShapeInfo, labelsShapeInfo), 0,
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"SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: labels and logits arrays must have the same shapes, but got "
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"%s and %s correspondingly!",
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ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
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auto outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
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auto reducedShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(labelsShapeInfo), &dimensions, labelsShapeInfo,
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outType, false, false, block.getWorkspace());
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return SHAPELIST(reducedShapeInfo);
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(softmax_cross_entropy_loss_with_logits_grad, 2, 2, false, 0, 0) {
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auto logits = INPUT_VARIABLE(0);
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auto labels = INPUT_VARIABLE(1);
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auto output = OUTPUT_VARIABLE(0);
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auto dLdp = OUTPUT_VARIABLE(0); // dL/dlogits
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auto dLdl = OUTPUT_VARIABLE(1); // dL/dlabels
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const int classesDim = block.getIArguments()->size() > 0 ? INT_ARG(0) : logits->rankOf()-1;
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// input validation
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REQUIRE_TRUE(labels->isSameShape(logits), 0,
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"SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: labels and logits arrays must have the same shapes, "
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"but got %s and %s correspondingly !",
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ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(logits).c_str());
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REQUIRE_TRUE(classesDim < logits->rankOf(), 0,
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"SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: class dimension must be smaller than rank of logits, "
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"but got %i and %i correspondingly !",
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classesDim, logits->rankOf());
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std::vector<LongType> dimension = {classesDim};
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// Compute softmax
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NDArray* maxAlongDim_ptr = logits->reduceAlongDimension(reduce::Max, &dimension, true);
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NDArray maxAlongDim = *maxAlongDim_ptr;
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delete maxAlongDim_ptr;
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NDArray* shiftedLogits_ptr = (*logits) - maxAlongDim;
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NDArray* softmax_ptr = shiftedLogits_ptr->transform(transform::Exp);
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delete shiftedLogits_ptr;
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NDArray softmax = *softmax_ptr;
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delete softmax_ptr;
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NDArray* sumSoftmax_ptr = softmax.reduceAlongDimension(reduce::Sum, &dimension, true);
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NDArray sumSoftmax = *sumSoftmax_ptr;
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delete sumSoftmax_ptr;
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softmax /= sumSoftmax;
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// dEdp = softmax * sum_i(labels_i) - labels
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// note the eps is to account for exact 0s in the log calculation being nan
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NDArray* labelsPlusEps_ptr = (*labels) + 1e-6;
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NDArray labelsPlusEps = *labelsPlusEps_ptr;
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delete labelsPlusEps_ptr;
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NDArray* labelSum_ptr = labelsPlusEps.reduceAlongDimension(reduce::Sum, &dimension, true);
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NDArray labelSum = *labelSum_ptr;
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delete labelSum_ptr;
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NDArray* softmaxTimesLabelSum_ptr = softmax * labelSum;
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NDArray* dLdpTemp_ptr = (*softmaxTimesLabelSum_ptr) - labelsPlusEps;
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delete softmaxTimesLabelSum_ptr;
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dLdp->assign(dLdpTemp_ptr);
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delete dLdpTemp_ptr;
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// dEdl = -log(softmax)
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softmax.applyTransform(transform::Log, dLdl);
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dLdl->applyTransform(transform::Neg, dLdl);
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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_with_logits_grad) {
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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(softmax_cross_entropy_loss_with_logits_grad) {
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auto logitsShapeInfo = inputShape->at(0);
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auto labelsShapeInfo = inputShape->at(1);
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// labels and logits must have the same shapes
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REQUIRE_TRUE(shape::shapeEquals(logitsShapeInfo, labelsShapeInfo), 0,
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"SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: labels and logits arrays must have the same shapes, "
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"but got %s and %s correspondingly!",
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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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auto dLdpShapeInfo = ConstantShapeHelper::getInstance().bufferForShapeInfo(outType, shape::order(logitsShapeInfo),
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shape::rank(logitsShapeInfo),
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shape::shapeOf(logitsShapeInfo))->primary();
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auto dLdlShapeInfo = 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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return SHAPELIST(dLdpShapeInfo, 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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