cavis/libnd4j/include/ops/declarable/generic/loss/sparseSoftmaxCrossEntropyWi...

168 lines
7.6 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Yurii Shyrma (iuriish@yahoo.com), created on 29.08.2018
//
#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_sparse_softmax_cross_entropy_loss_with_logits)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/generic/helpers/ScatterHelper.h>
namespace sd {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(sparse_softmax_cross_entropy_loss_with_logits, 2, 1, false, 0, 0) {
auto labels = INPUT_VARIABLE(0);
auto logits = INPUT_VARIABLE(1);
auto output = OUTPUT_VARIABLE(0);
const int labelsRank = labels->rankOf();
const int logitsRank = logits->rankOf();
// input validation
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);
std::vector<Nd4jLong> labelsShape = labels->getShapeAsVector(); // this is correct
std::vector<Nd4jLong> logitsShape = logits->getShapeAsVector();
logitsShape.pop_back();
bool equalSoft = logitsShape == labelsShape;
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());
std::vector<int> dimension = {-1};
auto maxAlongDim = logits->reduceAlongDimension(reduce::Max, dimension, true);
auto logitsExp = (*logits - maxAlongDim).transform(transform::Exp, nullptr);
auto logSoftMax = -(( logitsExp / logitsExp.reduceAlongDimension(reduce::Sum, dimension, true) ).transform(transform::Log));
helpers::scatterForLoss(block.launchContext(), *labels, logSoftMax, *output, false);
return Status::OK();
}
//////////////////////////////////////////////////////////////////////////
DECLARE_TYPES(sparse_softmax_cross_entropy_loss_with_logits) {
getOpDescriptor()->setAllowedInputTypes(0, {ALL_INTS})->setAllowedInputTypes(1, {ALL_FLOATS})->setAllowedOutputTypes({ALL_FLOATS});
}
//////////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(sparse_softmax_cross_entropy_loss_with_logits) {
auto labelsShapeInfo = inputShape->at(0);
auto logitsShapeInfo = inputShape->at(1);
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]);
bool equalSoft = true;
for (int i = 1; i < labelsShapeInfo[0]; ++i)
if (labelsShapeInfo[i] != logitsShapeInfo[i]) {
equalSoft = false;
break;
}
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());
auto outShapeInfo = ShapeBuilders::copyShapeInfoAndType(labelsShapeInfo, logitsShapeInfo, false, block.getWorkspace());
return SHAPELIST(CONSTANT(outShapeInfo));
}
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(sparse_softmax_cross_entropy_loss_with_logits_grad, 2, 1, false, 0, 0) {
auto labels = INPUT_VARIABLE(0);
auto logits = INPUT_VARIABLE(1);
auto dLdp = OUTPUT_VARIABLE(0); // dL/dlogits
const int labelsRank = labels->rankOf();
const int logitsRank = logits->rankOf();
// input validation
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);
std::vector<Nd4jLong> labelsShape = labels->getShapeAsVector(); // this is correct
std::vector<Nd4jLong> logitsShape = logits->getShapeAsVector();
logitsShape.pop_back();
bool equalSoft = logitsShape == labelsShape;
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());
std::vector<int> dimension = {-1};
NDArray softmax = (*logits - logits->reduceAlongDimension(reduce::Max, dimension, true)).transform(transform::Exp);
softmax /= softmax.reduceAlongDimension(reduce::Sum, dimension, true);
// dEdp = softmax - 1 (or 0)
dLdp->assign(softmax);
// subtract unities at appropriate indexes of dLdp array
helpers::scatterForLoss(block.launchContext(), *labels, *dLdp, *labels /*actually third array is unnecessary for gradient calculation*/, true);
return Status::OK();
}
//////////////////////////////////////////////////////////////////////////
DECLARE_TYPES(sparse_softmax_cross_entropy_loss_with_logits_grad) {
getOpDescriptor()->setAllowedInputTypes(0, {ALL_INTS})->setAllowedInputTypes(1, {ALL_FLOATS})->setAllowedOutputTypes({ALL_FLOATS});
}
//////////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(sparse_softmax_cross_entropy_loss_with_logits_grad) {
auto labelsShapeInfo = inputShape->at(0);
auto logitsShapeInfo = inputShape->at(1);
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]);
bool equalSoft = true;
for (int i = 1; i < labelsShapeInfo[0]; ++i)
if (labelsShapeInfo[i] != logitsShapeInfo[i]) {
equalSoft = false;
break;
}
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());
DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
Nd4jLong *dLdpShapeInfo = ShapeBuilders::copyShapeInfoAndType(logitsShapeInfo, outType, false, block.getWorkspace());
return SHAPELIST(CONSTANT(dLdpShapeInfo));
}
}
}
#endif