401 lines
16 KiB
C++
401 lines
16 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 19.04.2018
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// @author raver119@gmail.com
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//
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#include <ops/declarable/helpers/activations.h>
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#include <ShapeUtils.h>
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#include <numeric>
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#include <ConstantTadHelper.h>
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#include <execution/Threads.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo) {
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T* inBuff = reinterpret_cast<T *>(input);
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T* outBuff = reinterpret_cast<T *>(output);
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T max = -DataTypeUtils::max<T>();
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T sum = 0.;
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int inEWS = shape::elementWiseStride(inShapeInfo);
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int outEWS = shape::elementWiseStride(outShapeInfo);
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int length = shape::length(inShapeInfo);
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if (inEWS >= 1 && outEWS >= 1) {
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if (inEWS == 1 && outEWS == 1) {
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for (int i = 0; i < length; i++)
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max = nd4j::math::nd4j_max<T>(max, inBuff[i]);
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for (int i = 0; i < length; i++) {
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outBuff[i] = nd4j::math::nd4j_exp<T, T>(inBuff[i] - max);
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sum += outBuff[i];
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}
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PRAGMA_OMP_SIMD
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for (int i = 0; i < length; i++)
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outBuff[i] /= sum;
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}
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else {
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for (int i = 0; i < length; i++)
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max = nd4j::math::nd4j_max<T>(max, inBuff[i * inEWS]);
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for (int i = 0; i < length; i++) {
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T r = nd4j::math::nd4j_exp<T, T>(inBuff[i * inEWS] - max);
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outBuff[i * outEWS] = r;
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sum += r;
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}
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PRAGMA_OMP_SIMD
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for (int i = 0; i < length; i++)
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outBuff[i * outEWS] /= sum;
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}
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}
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}
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///////////////////////////////////////////////////////////////////
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template <typename T>
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void static _softMaxDerivForVector(nd4j::LaunchContext * context, const void *input, const Nd4jLong *inShapeInfo, void *output) {
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const T* inBuff = reinterpret_cast<const T *>(input);
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T* outBuff = reinterpret_cast<T *>(output);
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T max = -DataTypeUtils::max<T>();
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T sum = 0.;
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int length = shape::length(inShapeInfo);
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for (int i = 0; i < length; i++) {
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const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo);
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max = nd4j::math::nd4j_max<T>(max, inBuff[offset]);
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}
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for (int i = 0; i < length; i++) {
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const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo);
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outBuff[offset] = nd4j::math::nd4j_exp<T, T>(inBuff[offset] - max);
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sum += outBuff[offset];
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}
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for (int i = 0; i < length; i++) {
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const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo);
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outBuff[offset] /= sum;
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outBuff[offset] *= (1.f - outBuff[offset]); // derivative
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}
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}
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///////////////////////////////////////////////////////////////////
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void softmaxDerivative(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
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const int rank = input.rankOf();
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int temp;
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if(shape::isCommonVector(input.getShapeInfo(), temp)) {
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BUILD_SINGLE_SELECTOR(input.dataType(), _softMaxDerivForVector, (context, input.getBuffer(), input.getShapeInfo(), output.buffer()), FLOAT_TYPES);
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}
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else {
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auto maxAlongDim = const_cast<NDArray&>(input).reduceAlongDims(reduce::Max, {dimension}, true);
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(input - maxAlongDim).applyTransform(transform::Exp, &output); // output contains exponents temporarily
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auto sumAlongDim = output.reduceAlongDims(reduce::Sum, {dimension}, true);
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output /= sumAlongDim;
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output *= (1.f - output); // derivative
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}
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}
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///////////////////////////////////////////////////////////////////
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void softMaxForVector(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) {
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if(!input.isVector() || !output.isVector())
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throw std::runtime_error("ops::helpers::softMaxForVector function: input and output arrays must be vectors !");
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auto xType = input.dataType();
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BUILD_SINGLE_SELECTOR(xType, softMaxForVector_, (input.getBuffer(), input.getShapeInfo(), output.buffer(), output.shapeInfo()), FLOAT_TYPES);
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}
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///////////////////////////////////////////////////////////////////
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template <typename T>
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void logSoftMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo) {
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auto inBuff = reinterpret_cast<T *>(input);
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auto outBuff = reinterpret_cast<T *>(output);
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T max = -DataTypeUtils::max<T>();
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T sum = 0;
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auto inEWS = shape::elementWiseStride(inShapeInfo);
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auto length = shape::length(inShapeInfo);
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if (inEWS == 1) {
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for (int i = 0; i < length; i++)
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max = nd4j::math::nd4j_max<T>(max, inBuff[i]);
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PRAGMA_OMP_SIMD_SUM(sum)
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for (int i = 0; i < length; i++) {
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outBuff[i] = nd4j::math::nd4j_exp<T,T>(inBuff[i] - max);
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sum += outBuff[i];
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}
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PRAGMA_OMP_SIMD
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for (int i = 0; i < length; i++) {
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outBuff[i] /= sum;
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outBuff[i] = nd4j::math::nd4j_log<T,T>(outBuff[i]);
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}
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}
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else if (inEWS > 1) {
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PRAGMA_OMP_SIMD_MAX(max)
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for (int i = 0; i < length; i++)
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max = nd4j::math::nd4j_max<T>(max, inBuff[i * inEWS]);
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PRAGMA_OMP_SIMD_SUM(sum)
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for (int i = 0; i < length; i++) {
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outBuff[i * inEWS] = nd4j::math::nd4j_exp<T,T>(inBuff[i * inEWS] - max);
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sum += outBuff[i * inEWS];
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}
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PRAGMA_OMP_SIMD
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for (int i = 0; i < length; i++) {
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outBuff[i * inEWS] /= sum;
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outBuff[i * inEWS] = nd4j::math::nd4j_log<T, T>(outBuff[i * inEWS]);
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}
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}
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}
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///////////////////////////////////////////////////////////////////
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void logSoftMaxForVector(nd4j::LaunchContext * context, const NDArray& input, NDArray& output) {
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if(!input.isVector() || !output.isVector())
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throw std::runtime_error("ops::helpers::logSoftMaxForVector function input and output arrays must be vectors !");
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auto xType = input.dataType();
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BUILD_SINGLE_SELECTOR(xType, logSoftMaxForVector_, (input.getBuffer(), input.getShapeInfo(), output.buffer(), output.shapeInfo()), FLOAT_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void softmax_(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
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const int rank = input.rankOf();
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if(input.isVector()) {
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if(rank == 1 || input.sizeAt(dimension) != 1)
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softMaxForVector_<T>(input.getBuffer(), input.getShapeInfo(), output.buffer(), output.getShapeInfo());
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else
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output = 1.;
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}
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else if(input.isSameShapeStrict(&output)) {
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TadPack tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimension);
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Nd4jLong* tadShapeInfo = tadPack.primaryShapeInfo();
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Nd4jLong* tadOffsets = tadPack.primaryOffsets();
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const uint numOfSubArrs = tadPack.numberOfTads();
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const uint tadLen = shape::length(tadShapeInfo);
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if(shape::elementWiseStride(tadShapeInfo) == 1){
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) {
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T *inBuff = input.bufferAsT<T>() + tadOffsets[i];
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T *outBuff = output.bufferAsT<T>() + tadOffsets[i];
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T max = -DataTypeUtils::max<T>();
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T sum = 0;
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for (uint j = 0; j < tadLen; ++j)
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max = nd4j::math::nd4j_max<T>(max, inBuff[j]);
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for (uint j = 0; j < tadLen; ++j) {
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T temp = nd4j::math::nd4j_exp<T, T>(inBuff[j] - max);
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outBuff[j] = temp;
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sum += temp;
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}
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for (uint j = 0; j < tadLen; ++j)
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outBuff[j] /= sum;
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}
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};
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samediff::Threads::parallel_tad(func,0, numOfSubArrs);
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}
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else {
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uint inShapeInfoCast[MAX_RANK];
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bool canCast = nd4j::DataTypeUtils::castShapeInfo(tadShapeInfo, inShapeInfoCast);
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auto offsets = new Nd4jLong[tadLen];
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shape::calcOffsets(tadShapeInfo, offsets);
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) {
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auto inBuff = input.bufferAsT<T>() + tadOffsets[i];
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auto outBuff = output.bufferAsT<T>() + tadOffsets[i];
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T max = -DataTypeUtils::max<T>();
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T sum = 0.f;
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for (uint j = 0; j < tadLen; ++j)
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max = nd4j::math::nd4j_max<T>(max, inBuff[offsets[j]]);
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for (uint j = 0; j < tadLen; ++j) {
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T temp = nd4j::math::nd4j_exp<T, T>(inBuff[offsets[j]] - max);
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outBuff[offsets[j]] = temp;
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sum += temp;
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}
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for (uint j = 0; j < tadLen; ++j)
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outBuff[offsets[j]] /= sum;
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}
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};
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samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
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delete []offsets;
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}
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}
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else {
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NDArray max = input.reduceAlongDims(nd4j::reduce::Max, {dimension}, true);
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input.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), &max, &output, false);
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output.applyTransform(nd4j::transform::Exp);
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NDArray sum = output.reduceAlongDims(nd4j::reduce::Sum, {dimension}, true);
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output /= sum;
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}
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}
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///////////////////////////////////////////////////////////////////
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void softmax(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
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BUILD_SINGLE_SELECTOR(input.dataType(), softmax_, (context, input, output, dimension), FLOAT_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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void prelu(nd4j::LaunchContext * context, const NDArray& input, const NDArray& alpha, NDArray& output) {
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const Nd4jLong inputLen = input.lengthOf();
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const Nd4jLong* inputShapeInfo = input.getShapeInfo();
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const Nd4jLong* alphaShapeInfo = alpha.getShapeInfo();
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auto func = PRAGMA_THREADS_FOR {
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for (auto i = start; i < stop; i += increment) {
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// FIXME: double!
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double x = input.e<double>(i);
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if (x < 0.0) {
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// FIXME: double
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output.p(i, (x * alpha.e<double>(shape::subArrayIndex(i, inputShapeInfo, alphaShapeInfo))));
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} else
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output.p(i, x);
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}
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};
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samediff::Threads::parallel_for(func, 0, inputLen);
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}
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//////////////////////////////////////////////////////////////////////////
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void preluBP(nd4j::LaunchContext * context, const NDArray& input, const NDArray& alpha, const NDArray& dLdO, NDArray& dLdI, NDArray& dLdA) {
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const Nd4jLong inputLen = input.lengthOf();
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const Nd4jLong* inputShapeInfo = input.getShapeInfo();
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const Nd4jLong* alphaShapeInfo = alpha.getShapeInfo();
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dLdA.assign(0.0f);
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for(Nd4jLong i = 0; i < inputLen; ++i) {
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// FIXME: double
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double x = input.e<double>(i);
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double grO = dLdO.e<double>(i);
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if(x < 0.0) {
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Nd4jLong alphaInd = shape::subArrayIndex(i, inputShapeInfo, alphaShapeInfo);
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dLdI.p(i, grO * alpha.e<double>(alphaInd));
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double prevVal = dLdA.e<double>(alphaInd);
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prevVal += (grO * x);
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dLdA.p(alphaInd, prevVal);
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}
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else
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dLdI.p(i, grO);
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}
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}
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bool checkAlphaShapeLen(std::vector<Nd4jLong> const& expectedShape, Nd4jLong shapeLen) {
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Nd4jLong expectedAlphaLen = std::accumulate(expectedShape.cbegin(), expectedShape.cend(), 1, std::multiplies<Nd4jLong>());
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return expectedAlphaLen == shapeLen;
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}
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template <typename T>
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static void thresholdRelu_(NDArray const& input, double threshold, NDArray& output) {
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auto routine = LAMBDA_T(_x, threshold) {
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return _x > (T)threshold? _x: (T)0.f;
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};
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const_cast<NDArray&>(input).applyLambda<T>(routine, &output);
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}
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void thresholdRelu(nd4j::LaunchContext * context, NDArray const& input, double threshold, NDArray& output) {
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BUILD_SINGLE_SELECTOR(input.dataType(), thresholdRelu_, (input, threshold, output), FLOAT_TYPES);
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}
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template <typename T>
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static void thresholdReluDerivative_(nd4j::LaunchContext * context, NDArray* input, double theta, NDArray* dLdO, NDArray* output) {
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auto derivative = LAMBDA_TT(_x, grO, theta) {if (_x > theta) return grO; else return static_cast<T>(0); };
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input->applyPairwiseLambda<T>(dLdO, derivative, output);
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}
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void thresholdReluDerivative(nd4j::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), thresholdReluDerivative_, (context, input, threshold, dLdO, output), FLOAT_TYPES);
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}
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///////////////////////////////////////////////////////////////////
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void logSoftmax(nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
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const int rank = input.rankOf();
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if(input.isVector()) {
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if(rank == 1 || input.sizeAt(dimension) != 1) {
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BUILD_SINGLE_SELECTOR(input.dataType(), logSoftMaxForVector_, (input.getBuffer(), input.getShapeInfo(), output.buffer(), output.shapeInfo()), FLOAT_TYPES);
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}
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else
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output = 0.;
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}
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else {
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auto maxAlongDim = const_cast<NDArray&>(input).reduceAlongDims(reduce::Max, {dimension}, true);
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(input - maxAlongDim).applyTransform(transform::Exp, &output); // output contains exponents temporarily
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auto sumAlongDim = output.reduceAlongDims(reduce::Sum, {dimension}, true);
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output /= sumAlongDim;
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output.applyTransform(transform::Log);
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}
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}
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BUILD_SINGLE_TEMPLATE(template void thresholdReluDerivative_, (nd4j::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output), FLOAT_TYPES);
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BUILD_SINGLE_TEMPLATE(template void softmax_, (nd4j::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension), FLOAT_TYPES);
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BUILD_SINGLE_TEMPLATE(template void logSoftMaxForVector_, (void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo), FLOAT_TYPES);
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BUILD_SINGLE_TEMPLATE(template void _softMaxDerivForVector, (nd4j::LaunchContext * context, const void *input, const Nd4jLong *inShapeInfo, void *output), FLOAT_TYPES);
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}
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}
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}
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