wehub-resource-sync
97 lines
4.2 KiB
C++
97 lines
4.2 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 AbdelRauf (rauf@konduit.ai)
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//
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// CPU implementation of summary stat reductions (variance, standardDeviation)
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//
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#include <helpers/ConstantTadHelper.h>
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#include <legacy/NativeOpExecutioner.h>
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#include <ops/declarable/helpers/reductions.h>
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#include <system/op_enums.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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void variance(NDArray& input, NDArray& output, const std::vector<LongType>& dimensions, bool biasCorrected) {
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// Prepares (syncs) buffer of which NDArrays will be used as read, write
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NDArray::prepareSpecialUse({&output}, {&input});
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if (output.isScalar()) {
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NativeOpExecutioner::execSummaryStatsScalar(
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LaunchContext::defaultContext(), variance::SummaryStatsVariance,
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input.buffer(), input.shapeInfo(), input.specialBuffer(), input.specialShapeInfo(),
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nullptr,
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output.buffer(), output.shapeInfo(), output.specialBuffer(), output.specialShapeInfo(),
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biasCorrected);
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} else {
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auto tadPack = ConstantTadHelper::getInstance().tadForDimensions(
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input.shapeInfo(), const_cast<sd::LongType*>(dimensions.data()), dimensions.size());
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NativeOpExecutioner::execSummaryStats(
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LaunchContext::defaultContext(), variance::SummaryStatsVariance,
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input.buffer(), input.shapeInfo(), input.specialBuffer(), input.specialShapeInfo(),
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nullptr,
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output.buffer(), output.shapeInfo(), output.specialBuffer(), output.specialShapeInfo(),
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const_cast<LongType*>(dimensions.data()), dimensions.size(),
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tadPack->primaryShapeInfo(), tadPack->primaryOffsets(),
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biasCorrected);
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}
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// Inform that we are done with those buffers
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NDArray::registerSpecialUse({&output}, {&input});
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}
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//////////////////////////////////////////////////////////////////////////
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void standardDeviation(NDArray& input, NDArray& output, const std::vector<LongType>& dimensions, bool biasCorrected) {
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// Prepares (syncs) buffer of which NDArrays will be used as read, write
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NDArray::prepareSpecialUse({&output}, {&input});
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if (output.isScalar()) {
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NativeOpExecutioner::execSummaryStatsScalar(
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LaunchContext::defaultContext(), variance::SummaryStatsStandardDeviation,
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input.buffer(), input.shapeInfo(), input.specialBuffer(), input.specialShapeInfo(),
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nullptr,
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output.buffer(), output.shapeInfo(), output.specialBuffer(), output.specialShapeInfo(),
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biasCorrected);
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} else {
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auto tadPack = ConstantTadHelper::getInstance().tadForDimensions(
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input.shapeInfo(), const_cast<sd::LongType*>(dimensions.data()), dimensions.size());
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NativeOpExecutioner::execSummaryStats(
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LaunchContext::defaultContext(), variance::SummaryStatsStandardDeviation,
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input.buffer(), input.shapeInfo(), input.specialBuffer(), input.specialShapeInfo(),
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nullptr,
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output.buffer(), output.shapeInfo(), output.specialBuffer(), output.specialShapeInfo(),
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const_cast<LongType*>(dimensions.data()), dimensions.size(),
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tadPack->primaryShapeInfo(), tadPack->primaryOffsets(),
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biasCorrected);
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}
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// Inform that we are done with those buffers
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NDArray::registerSpecialUse({&output}, {&input});
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}
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} // namespace helpers
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} // namespace ops
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} // namespace sd
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