wehub-resource-sync
476 lines
20 KiB
C++
476 lines
20 KiB
C++
/* ******************************************************************************
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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 22.11.2017
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_cosine_distance_loss)
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#include <helpers/ShapeUtils.h>
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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(cosine_distance_loss, 3, 1, false, 0, 2) {
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auto predictions = 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 =
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INT_ARG(0); // 0 - "none"; 1 - "weighted_sum"; 2 - "weighted_mean"; 3 - "weighted_sum_by_nonzero_weights"
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int dim = INT_ARG(1); // axis along which sum will be made
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if (dim < 0) dim += labels->rankOf();
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// labels and predictions must have the same shapes
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REQUIRE_TRUE(labels->isSameShape(predictions), 0,
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"COSINE_DISTANCE_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and %s "
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"correspondingly !",
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ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(predictions).c_str());
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// regard 4 possible reduction modes below
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REQUIRE_TRUE(reductionMode == 0 || reductionMode == 1 || reductionMode == 2 || reductionMode == 3, 0,
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"COSINE_DISTANCE_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but "
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"got %i instead!",
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reductionMode);
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// input dimension can't be larger than labels/predictions/weights rank
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REQUIRE_TRUE(dim < labels->rankOf(), 0,
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"COSINE_DISTANCE_LOSS OP: input reduction dimension (got %i) must be < labels rank %i!", dim,
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labels->rankOf());
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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,
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"SOFTMAX_CROSS_ENTROPY_LOSS OP: weights array should be scalar or have the same rank as output array, "
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"but got %i and %i correspondingly!",
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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,
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"COSINE_DISTANCE_LOSS OP: shapes of weights and output arrays should be broadcastable, but got "
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"weights = %s and output = %s instead!",
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ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
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}
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std::vector<LongType> dims;
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dims.push_back(dim);
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NDArray* predLabels = (*predictions) * (*labels);
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NDArray* dotProduct = predLabels->reduceAlongDimension(reduce::Sum, &dims, true);
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delete predLabels;
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NDArray* E = 1. - (*dotProduct);
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delete dotProduct;
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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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// multiply E on weights
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NDArray* EWeighted = (*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(EWeighted);
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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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auto* sumResult = EWeighted->reduceNumber(reduce::Sum);
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output->assign(sumResult);
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delete sumResult;
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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
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// all elements of weightsBroad array
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NDArray* sum;
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if (weights->isScalar()) {
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sum = (*weights) * EWeighted->lengthOf();
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} else {
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sum = weightsBroad->reduceNumber(reduce::Sum);
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}
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if (sum->e<double>(0) == 0.) {
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*output = 0.;
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} else {
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auto* sumE = EWeighted->reduceNumber(reduce::Sum);
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auto* result = (*sumE) / (*sum);
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output->assign(result);
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delete result;
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delete sumE;
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}
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delete 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
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// array divided by number of non-zero weights
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LongType numOfNonZeroWeights = 0;
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if (weights->isScalar()) {
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if (weights->e<double>(0) != 0.) numOfNonZeroWeights = EWeighted->lengthOf();
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} else {
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auto* countResult = EWeighted->reduceNumber(reduce::CountNonZero);
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numOfNonZeroWeights = countResult->e<LongType>(0);
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delete countResult;
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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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auto* sumE = EWeighted->reduceNumber(reduce::Sum);
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auto* result = (*sumE) / double(numOfNonZeroWeights);
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output->assign(result);
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delete result;
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delete sumE;
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}
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break;
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}
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}
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STORE_RESULT(*output);
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delete EWeighted;
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delete E;
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if (weightsBroad != weights) delete weightsBroad;
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return Status::OK;
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_TYPES(cosine_distance_loss) {
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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(cosine_distance_loss) {
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// labels and predictions must have the same shapes
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auto predictionsShapeInfo = 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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int dim = INT_ARG(1);
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if (dim < 0) dim += labelsShapeInfo[0];
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// labels and predictions must have the same shapes
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REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0,
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"COSINE_DISTANCE_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and %s "
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"correspondingly !",
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ShapeUtils::shapeAsString(labelsShapeInfo).c_str(),
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ShapeUtils::shapeAsString(predictionsShapeInfo).c_str());
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// input dimension can't be larger than labels/predictions/weights rank
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REQUIRE_TRUE(dim < labelsShapeInfo[0], 0,
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"COSINE_DISTANCE_LOSS OP: input reduction dimension (got %i) must be < labels rank %i!", dim,
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labelsShapeInfo[0]);
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DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(predictionsShapeInfo));
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// evaluate output shapeInfo
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LongType * 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 same shape as labels reduced by dim axis
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std::vector<LongType> dimensions = {dim};
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outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(predictionsShapeInfo), &dimensions, predictionsShapeInfo,
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outType, true, false, 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,
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"COSINE_DISTANCE_LOSS OP: weights array should be scalar or have the same rank as output array, but "
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"got %i and %i correspondingly!",
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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(
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shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, outShapeInfo), 0,
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"COSINE_DISTANCE_LOSS OP: shapes of weights and output arrays should be broadcastable, but got weights = %s "
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"and output = %s instead!",
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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(cosine_distance_loss_grad, 3, 3, false, 0, 2) {
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auto predictions = 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/dpredictions
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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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int reductionMode =
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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) reductionMode = 1;
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int dim = INT_ARG(1); // axis along which sum will be made
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if (dim < 0) dim += labels->rankOf();
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std::vector<LongType> dimensions = {dim};
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// input validation
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REQUIRE_TRUE(labels->isSameShape(predictions), 0,
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"COSINE_DISTANCE_LOSS_GRAD OP: labels and predictions arrays must have the same shapes, but got %s and "
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"%s correspondingly !",
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ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(predictions).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,
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"COSINE_DISTANCE_LOSS_GRAD OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, "
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"but got %i instead!",
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reductionMode);
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auto lossShapeInfo = ShapeUtils::evalReduceShapeInfo(predictions->ordering(), &dimensions, predictions->shapeInfo(),
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true, 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,
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"COSINE_DISTANCE_LOSS_GRAD OP: weights array should be scalar or have the same rank as loss array, but "
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"got %i and %i correspondingly!",
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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,
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"COSINE_DISTANCE_LOSS_GRAD OP: shapes of weights and loss arrays should be broadcastable, but got "
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"weights = %s and loss = %s instead!",
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ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(lossShapeInfo).c_str());
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// input dimension can't be larger than labels/predictions/weights rank
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REQUIRE_TRUE(dim < labels->rankOf(), 0,
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"COSINE_DISTANCE_LOSS_GRAD OP: input reduction dimension (got %i) must be < labels rank %i!", dim,
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labels->rankOf());
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std::vector<LongType> dims;
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dims.push_back(dim);
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NDArray* predLabels = (*predictions) * (*labels);
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NDArray* dotProduct = predLabels->reduceAlongDimension(reduce::Sum, &dims, true);
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delete predLabels;
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NDArray* E = 1. - (*dotProduct);
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delete dotProduct;
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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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NDArray negLabels = -(*labels);
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NDArray negPreds = -(*predictions);
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dLdp->assign(&negLabels);
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dLdl->assign(&negPreds);
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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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NDArray* dLdpWeighted = (*dLdp) * (*weightsBroad);
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dLdp->assign(dLdpWeighted);
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delete dLdpWeighted;
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NDArray* dLdlWeighted = (*dLdl) * (*weightsBroad);
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dLdl->assign(dLdlWeighted);
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delete dLdlWeighted;
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if (weights->isScalar() || weights->lengthOf() == 1) {
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auto* sumE = E->reduceNumber(reduce::Sum);
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dLdw->assign(sumE);
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delete sumE;
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} else {
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if (weights != weightsBroad) {
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std::vector<LongType> axesToReduceAlong =
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ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
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E->reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
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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
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// 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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}
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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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} else {
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NDArray* temp = (*weightsBroad) / (*sum);
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NDArray* dLdpResult = (*dLdp) * (*temp);
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dLdp->assign(dLdpResult);
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delete dLdpResult;
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NDArray* dLdlResult = (*dLdl) * (*temp);
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dLdl->assign(dLdlResult);
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delete dLdlResult;
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delete temp;
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if (weights->isScalar() || weights->lengthOf() == 1) {
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*dLdw = 0.;
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} else {
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if (weights != weightsBroad) {
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std::vector<LongType> axesToReduceAlong =
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ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
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NDArray* EWeighted = (*E) * (*weightsBroad);
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NDArray* EWeightedSum = EWeighted->reduceNumber(reduce::Sum);
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delete EWeighted;
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NDArray* ESum = (*E) * (*sum);
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NDArray* numerator = (*ESum) - (*EWeightedSum);
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delete ESum;
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delete EWeightedSum;
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NDArray* sumSquared = (*sum) * (*sum);
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NDArray* gradTemp = (*numerator) / (*sumSquared);
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delete numerator;
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delete sumSquared;
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gradTemp->reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
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delete gradTemp;
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} else {
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NDArray* EWeighted = (*E) * (*weightsBroad);
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NDArray* EWeightedSum = EWeighted->reduceNumber(reduce::Sum);
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delete EWeighted;
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NDArray* ESum = (*E) * (*sum);
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NDArray* numerator = (*ESum) - (*EWeightedSum);
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delete ESum;
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delete EWeightedSum;
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NDArray* sumSquared = (*sum) * (*sum);
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NDArray* gradTemp = (*numerator) / (*sumSquared);
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delete numerator;
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delete sumSquared;
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dLdw->assign(gradTemp);
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delete gradTemp;
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}
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}
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}
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delete 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
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// array divided by number of non-zero weights
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LongType numOfNonZeroWeights = 0;
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if (weights->isScalar()) {
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if (weights->e<double>(0) != 0.) numOfNonZeroWeights = E->lengthOf();
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} else {
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auto* countResult = weightsBroad->reduceNumber(reduce::CountNonZero);
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numOfNonZeroWeights = countResult->e<LongType>(0);
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delete countResult;
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}
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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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} else {
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NDArray* temp = (*weightsBroad) / numOfNonZeroWeights;
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NDArray* dLdpResult = (*dLdp) * (*temp);
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dLdp->assign(dLdpResult);
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delete dLdpResult;
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NDArray* dLdlResult = (*dLdl) * (*temp);
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dLdl->assign(dLdlResult);
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delete dLdlResult;
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delete temp;
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if (weights->isScalar() || weights->lengthOf() == 1) {
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auto* sumE = E->reduceNumber(reduce::Sum);
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auto* result = (*sumE) / numOfNonZeroWeights;
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dLdw->assign(result);
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delete result;
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delete sumE;
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} else {
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if (weights != weightsBroad) {
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std::vector<LongType> axesToReduceAlong =
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ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
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E->reduceAlongDimension(reduce::Sum, dLdw, &axesToReduceAlong, true);
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NDArray* dLdwResult = (*dLdw) / numOfNonZeroWeights;
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dLdw->assign(dLdwResult);
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delete dLdwResult;
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} else {
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NDArray* result = (*E) / numOfNonZeroWeights;
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dLdw->assign(result);
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delete result;
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}
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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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delete E;
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if (weightsBroad != weights) delete weightsBroad;
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return Status::OK;
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_TYPES(cosine_distance_loss_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(cosine_distance_loss_grad) {
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/// labels and predictions must have the same shapes
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auto predictionsShapeInfo = 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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int dim = INT_ARG(1);
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if (dim < 0) dim += labelsShapeInfo[0];
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std::vector<LongType> dimensions = {dim};
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// labels and predictions must have the same shapes
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REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0,
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"COSINE_DISTANCE_LOSS_GRAD OP: labels and predictions arrays must have the same shapes, but got %s and "
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"%s correspondingly !",
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ShapeUtils::shapeAsString(labelsShapeInfo).c_str(),
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ShapeUtils::shapeAsString(predictionsShapeInfo).c_str());
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auto lossShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(predictionsShapeInfo), &dimensions,
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predictionsShapeInfo, true, 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,
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"COSINE_DISTANCE_LOSS_GRAD OP: weights array should be scalar or have the same rank as loss array, but "
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"got %i and %i correspondingly!",
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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),
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0,
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|
"COSINE_DISTANCE_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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// input dimension can't be larger than labels/predictions/weights rank
|
|
REQUIRE_TRUE(dim < labelsShapeInfo[0], 0,
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|
"COSINE_DISTANCE_LOSS_GRAD OP: input reduction dimension (got %i) must be < labels rank %i!", dim,
|
|
labelsShapeInfo[0]);
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|
|
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auto outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(predictionsShapeInfo));
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|
|
|
auto dLdpShapeInfo = ShapeBuilders::copyShapeInfoAndType(predictionsShapeInfo, outType, false, block.getWorkspace());
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|
auto dLdwShapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, outType, false, block.getWorkspace());
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|
auto dLdlShapeInfo = ShapeBuilders::copyShapeInfoAndType(labelsShapeInfo, outType, false, block.getWorkspace());
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|
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return SHAPELIST(CONSTANT(dLdpShapeInfo), CONSTANT(dLdwShapeInfo), CONSTANT(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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