168 lines
7.5 KiB
C++
168 lines
7.5 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 29.08.2018
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_sparse_softmax_cross_entropy_loss_with_logits)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/generic/helpers/ScatterHelper.h>
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namespace nd4j {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(sparse_softmax_cross_entropy_loss_with_logits, 2, 1, false, 0, 0) {
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auto labels = INPUT_VARIABLE(0);
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auto logits = INPUT_VARIABLE(1);
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auto output = OUTPUT_VARIABLE(0);
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const int labelsRank = labels->rankOf();
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const int logitsRank = logits->rankOf();
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// input validation
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REQUIRE_TRUE(labelsRank == logitsRank - 1, 0, "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: input arrays should satisfy relation (labels_rank = logits_rank - 1), but got labels_rank = %i and logits_rank = %i instead !", labelsRank, logitsRank);
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std::vector<Nd4jLong> labelsShape = labels->getShapeAsVector(); // this is correct
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std::vector<Nd4jLong> logitsShape = logits->getShapeAsVector();
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logitsShape.pop_back();
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bool equalSoft = logitsShape == labelsShape;
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REQUIRE_TRUE(equalSoft, 0, "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: wrong shape of labels array, its shape should be the same as logits shape with last dimension excluded, however got labels_shape = %s and logits_shape = %s instead !", ShapeUtils::shapeAsString(labelsShape).c_str(), ShapeUtils::shapeAsString(logitsShape).c_str());
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std::vector<int> dimension = {-1};
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auto maxAlongDim = logits->reduceAlongDims(reduce::Max, dimension, true);
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auto logitsExp = (*logits - maxAlongDim).transform(transform::Exp, nullptr);
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auto logSoftMax = -(( logitsExp / logitsExp.reduceAlongDims(reduce::Sum, dimension, true) ).transform(transform::Log));
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helpers::scatterForLoss(block.launchContext(), *labels, logSoftMax, *output, false);
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_TYPES(sparse_softmax_cross_entropy_loss_with_logits) {
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getOpDescriptor()->setAllowedInputTypes(0, {ALL_INTS})->setAllowedInputTypes(1, {ALL_FLOATS})->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_SHAPE_FN(sparse_softmax_cross_entropy_loss_with_logits) {
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auto labelsShapeInfo = inputShape->at(0);
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auto logitsShapeInfo = inputShape->at(1);
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REQUIRE_TRUE(labelsShapeInfo[0] == logitsShapeInfo[0] - 1, 0, "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: input arrays should satisfy relation (labels_rank = logits_rank - 1), but got labels_rank = %i and logits_rank = %i instead !", labelsShapeInfo[0], logitsShapeInfo[0]);
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bool equalSoft = true;
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for (int i = 1; i < labelsShapeInfo[0]; ++i)
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if (labelsShapeInfo[i] != logitsShapeInfo[i]) {
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equalSoft = false;
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break;
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}
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REQUIRE_TRUE(equalSoft, 0, "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: wrong shape of labels array, its shape should be the same as logits shape with last dimension excluded, however got labels_shape = %s and logits_shape = %s instead !", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
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auto outShapeInfo = ShapeBuilders::copyShapeInfoAndType(labelsShapeInfo, logitsShapeInfo, false, block.getWorkspace());
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return SHAPELIST(CONSTANT(outShapeInfo));
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(sparse_softmax_cross_entropy_loss_with_logits_grad, 2, 1, false, 0, 0) {
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auto labels = INPUT_VARIABLE(0);
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auto logits = INPUT_VARIABLE(1);
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auto dLdp = OUTPUT_VARIABLE(0); // dL/dlogits
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const int labelsRank = labels->rankOf();
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const int logitsRank = logits->rankOf();
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// input validation
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REQUIRE_TRUE(labelsRank == logitsRank - 1, 0, "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: input arrays should satisfy relation (labels_rank = logits_rank - 1), but got labels_rank = %i and logits_rank = %i instead !", labelsRank, logitsRank);
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std::vector<Nd4jLong> labelsShape = labels->getShapeAsVector(); // this is correct
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std::vector<Nd4jLong> logitsShape = logits->getShapeAsVector();
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logitsShape.pop_back();
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bool equalSoft = logitsShape == labelsShape;
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REQUIRE_TRUE(equalSoft, 0, "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: wrong shape of labels array, its shape should be the same as logits shape with last dimension excluded, however got labels_shape = %s and logits_shape = %s instead !", ShapeUtils::shapeAsString(labelsShape).c_str(), ShapeUtils::shapeAsString(logitsShape).c_str());
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std::vector<int> dimension = {-1};
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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 - 1 (or 0)
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dLdp->assign(softmax);
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// subtract unities at appropriate indexes of dLdp array
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helpers::scatterForLoss(block.launchContext(), *labels, *dLdp, *labels /*actually third array is unnecessary for gradient calculation*/, true);
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_TYPES(sparse_softmax_cross_entropy_loss_with_logits_grad) {
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getOpDescriptor()->setAllowedInputTypes(0, {ALL_INTS})->setAllowedInputTypes(1, {ALL_FLOATS})->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_SHAPE_FN(sparse_softmax_cross_entropy_loss_with_logits_grad) {
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auto labelsShapeInfo = inputShape->at(0);
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auto logitsShapeInfo = inputShape->at(1);
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REQUIRE_TRUE(labelsShapeInfo[0] == logitsShapeInfo[0] - 1, 0, "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: input arrays should satisfy relation (labels_rank = logits_rank - 1), but got labels_rank = %i and logits_rank = %i instead !", labelsShapeInfo[0], logitsShapeInfo[0]);
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bool equalSoft = true;
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for (int i = 1; i < labelsShapeInfo[0]; ++i)
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if (labelsShapeInfo[i] != logitsShapeInfo[i]) {
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equalSoft = false;
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break;
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}
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REQUIRE_TRUE(equalSoft, 0, "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: wrong shape of labels array, its shape should be the same as logits shape with last dimension excluded, however got labels_shape = %s and logits_shape = %s instead !", 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 *dLdpShapeInfo = ShapeBuilders::copyShapeInfoAndType(logitsShapeInfo, outType, false, block.getWorkspace());
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return SHAPELIST(CONSTANT(dLdpShapeInfo));
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}
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}
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}
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#endif |