141 lines
6.4 KiB
C++
141 lines
6.4 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 18.06.2018
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//
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#include <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 nd4j {
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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, "SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS 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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REQUIRE_TRUE(classesDim < logits->rankOf(), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: class dimension must be smaller than rank of logits, but got %i and %i correspondingly !", classesDim, logits->rankOf());
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std::vector<int> dimension = {classesDim};
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auto maxAlongDim = logits->reduceAlongDims(reduce::Max, {classesDim}, true);
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auto logExp = (*logits - maxAlongDim).transform(transform::Exp);
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auto logSoftMax = ( logExp / logExp.reduceAlongDims(reduce::Sum, {classesDim}, true) ).transform(transform::Log);
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(-(*labels) * logSoftMax).reduceAlongDimension(reduce::Sum, output, dimension);
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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(nd4j::DataType::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<int> 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, "SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS 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 outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
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auto reducedShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(labelsShapeInfo), dimensions, labelsShapeInfo, 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, "SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_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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REQUIRE_TRUE(classesDim < logits->rankOf(), 0, "SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: class dimension must be smaller than rank of logits, but got %i and %i correspondingly !", classesDim, logits->rankOf());
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std::vector<int> dimension = {classesDim};
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NDArray softmax = (*logits - logits->reduceAlongDims(reduce::Max, dimension, true)).transform(transform::Exp);
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softmax /= softmax.reduceAlongDims(reduce::Sum, dimension, true);
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// dEdp = softmax * sum_i(labels_i) - labels
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dLdp->assign(softmax * labels->reduceAlongDims(reduce::Sum, dimension, true) - *labels);
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// dEdl = -log(softmax)
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(-softmax).applyTransform(transform::Log, 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(nd4j::DataType::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, "SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_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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DataType 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 dLdlShapeInfo = ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(outType, shape::order(labelsShapeInfo), shape::shapeOf(labelsShapeInfo), shape::rank(labelsShapeInfo)));
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return SHAPELIST(dLdpShapeInfo, dLdlShapeInfo);
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}
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}
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}
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#endif |