209 lines
8.2 KiB
C++
209 lines
8.2 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 Yurii Shyrma (iuriish@yahoo.com)
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// @author sgazeos@gmail.com
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// @author raver119@gmail.com
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//
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#include <ops/declarable/helpers/transforms.h>
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#include <execution/Threads.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 clipByNorm(sd::LaunchContext* context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace, const bool useAverage) {
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NDArray* z = nullptr;
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if(isInplace) {
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z = &input;
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}
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else {
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output.assign(input);
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z = &output;
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}
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if(dimensions.empty()) {
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const NDArray actualNorm = useAverage ? z->reduceAlongDimension(reduce::Norm2, {}) / z->lengthOf() : z->reduceAlongDimension(reduce::Norm2, {});
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if(actualNorm.e<float>(0) > clipNorm.e<float>(0))
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*z *= clipNorm / actualNorm;
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}
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else {
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auto listOfSubArrs = z->allTensorsAlongDimension(dimensions);
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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const NDArray actualNorm = useAverage ? listOfSubArrs.at(i)->reduceAlongDimension(reduce::Norm2, {}) / listOfSubArrs.at(i)->lengthOf() : listOfSubArrs.at(i)->reduceAlongDimension(reduce::Norm2, {});
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if(actualNorm.e<float>(0) > clipNorm.e<float>(0))
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*listOfSubArrs.at(i) *= clipNorm / actualNorm;
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}
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};
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samediff::Threads::parallel_tad(func, 0, listOfSubArrs.size());
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename T>
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static void clipByNormBp_(const NDArray& input, const NDArray& gradO, NDArray& gradI, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool useAverage) {
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const int rank = input.rankOf();
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auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions);
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auto sums = input.reduceAlongDimension(reduce::Sum, dimensions);
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if(norm2.lengthOf() == 1) {
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const T norm = useAverage ? norm2.e<T>(0) / input.lengthOf() : norm2.e<T>(0);
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auto clipVal = clipNorm.e<T>(0);
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if(norm > clipVal) {
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const T sum = sums.e<T>(0); // reduce to scalar
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const T factor1 = clipVal / norm;
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const T factor2 = static_cast<T>(1.f) / (norm * norm); // 1 / (norm*norm*norm)
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auto lambda = LAMBDA_TT(x, y, sum, factor1, factor2) {
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return factor1 * y * (static_cast<T>(1.f) - factor2 * x * sum);
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};
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const_cast<NDArray&>(input).applyPairwiseLambda<T>(const_cast<NDArray&>(gradO), lambda, gradI);
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}
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else
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gradI.assign(gradO);
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}
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else {
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auto gradISubArrs = gradI.allTensorsAlongDimension({dimensions});
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auto gradOSubArrs = gradO.allTensorsAlongDimension({dimensions});
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auto inputSubArrs = input.allTensorsAlongDimension({dimensions});
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auto clipVal = clipNorm.e<T>(0);
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i++) {
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auto gradOSubArr = gradOSubArrs.at(i);
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auto gradISubArr = gradISubArrs.at(i);
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const T norm = useAverage ? norm2.e<T>(i) / gradISubArr->lengthOf() : norm2.e<T>(i);
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if (norm > clipVal) {
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auto inputSubArr = inputSubArrs.at(i);
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const T sum = sums.e<T>(i); // reduce to scalar
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const T factor1 = clipVal / norm;
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const T factor2 = static_cast<T>(1.f) / (norm * norm); // 1 / (norm*norm*norm)
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auto lambda = LAMBDA_TT(x, y, sum, factor1, factor2) {
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return factor1 * y * (static_cast<T>(1.f) - factor2 * x * sum);
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};
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inputSubArr->applyPairwiseLambda<T>(*gradOSubArr, lambda, *gradISubArr);
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}
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else
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gradISubArr->assign(gradOSubArr);
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}
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};
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samediff::Threads::parallel_tad(func, 0, gradISubArrs.size());
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}
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}
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BUILD_SINGLE_TEMPLATE(template void clipByNormBp_, (const NDArray& input, const NDArray& gradO, NDArray& gradI, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool useAverage), FLOAT_TYPES);
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//////////////////////////////////////////////////////////////////////////
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void clipByNormBp(sd::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool useAverage) {
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const NDArray& castedInput = gradI.dataType() == input.dataType() ? input : input.cast(gradI.dataType());
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BUILD_SINGLE_SELECTOR(gradI.dataType(), clipByNormBp_, (castedInput, gradO, gradI, dimensions, clipNorm, useAverage), FLOAT_TYPES);
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}
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template <typename T>
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static void clipByGlobalNorm_(std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
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T globalNorm = 0; //NDArrayFactory::create<T>(0, inputs[0]->getContext()); //sqrt(sum([l2norm(t)**2 for t in t_list]))
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// PRAGMA_OMP_PARALLEL_FOR_SIMD_REDUCTION(sumT : globalNorm)
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for (size_t i = 0; i < inputs.size(); i++) {
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auto input = inputs[i];
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auto l2norm = input->reduceNumber(reduce::Norm2);
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globalNorm += l2norm.t<T>(0) * l2norm.t<T>(0);
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}
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//globalNorm.applyTransform(transform::Sqrt, nullptr, nullptr);// = sd::math::nd4j_sqrt(globalNorm);
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auto normS = sd::math::nd4j_sqrt<T,T>(globalNorm);
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outputs[inputs.size()]->p(0, normS);
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const T factor = clipNorm / normS;
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// PRAGMA_OMP_PARALLEL_FOR
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for (size_t e = 0; e < inputs.size(); e++) {
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// all-reduce
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auto input = inputs[e];
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auto output = outputs[e];
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if (normS <= clipNorm) {
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output->assign(input);
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}
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else {
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auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
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input->applyLambda<T>(lambda, *output);
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}
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}
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}
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void clipByGlobalNorm(sd::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
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BUILD_SINGLE_SELECTOR(outputs[0]->dataType(), clipByGlobalNorm_, (inputs, clipNorm, workspace, outputs, isInplace), FLOAT_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace), FLOAT_TYPES);
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template <typename T>
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static void clipByValue_(NDArray& input, double leftBound, double rightBound, NDArray& output) {
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auto routine = LAMBDA_T(_x, leftBound, rightBound) {
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if (_x > rightBound) return rightBound;
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if (_x < leftBound) return leftBound;
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return _x;
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};
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input.applyLambda<T>(routine, output);
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}
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void clipByValue(sd::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) {
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BUILD_SINGLE_SELECTOR(input.dataType(), clipByValue_, (input, leftBound, rightBound, output), FLOAT_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void clipByValue_, (NDArray& input, double leftBound, double rightBound, NDArray& output);, FLOAT_TYPES);
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}
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}
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}
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