wehub-resource-sync
273 lines
10 KiB
C++
273 lines
10 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 19.04.2018
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// @author raver119@gmail.com
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//
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#include <execution/Threads.h>
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#include <helpers/ConstantTadHelper.h>
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#include <helpers/ShapeUtils.h>
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#include <ops/declarable/helpers/activations.h>
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#include <numeric>
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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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template <typename T>
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void static _softMaxDerivForVector(sd::LaunchContext* context, const void* input, const sd::LongType* inShapeInfo,
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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 = static_cast<T>(0.);
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const sd::LongType length = shape::length(inShapeInfo);
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const sd::LongType rank = shape::rank(inShapeInfo);
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const sd::LongType* shape = shape::shapeOf(inShapeInfo);
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const sd::LongType* stride = shape::stride(inShapeInfo);
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LongType coords[SD_MAX_RANK];
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LongType offset;
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// Find the maximum value in the vector
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for (sd::LongType i = 0; i < length; i++) {
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INDEX2COORDS(i, rank, shape, coords);
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COORDS2INDEX(rank, stride, coords, offset);
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max = sd::math::sd_max<T>(max, inBuff[offset]);
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}
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// Calculate exponentials and sum
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for (sd::LongType i = 0; i < length; i++) {
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INDEX2COORDS(i, rank, shape, coords);
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COORDS2INDEX(rank, stride, coords, offset);
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outBuff[offset] = sd::math::sd_exp<T, T>(inBuff[offset] - max);
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sum += outBuff[offset];
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}
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// Compute softmax derivatives
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for (sd::LongType i = 0; i < length; i++) {
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INDEX2COORDS(i, rank, shape, coords);
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COORDS2INDEX(rank, stride, coords, offset);
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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(sd::LaunchContext* context, NDArray& input, NDArray& output, const int dimension) {
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const int rank = input.rankOf();
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sd::LongType temp;
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if (shape::isCommonVector(input.shapeInfo(), temp)) {
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BUILD_SINGLE_SELECTOR(input.dataType(), _softMaxDerivForVector,
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(context, input.buffer(), input.shapeInfo(), output.buffer()), SD_FLOAT_TYPES);
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} else {
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std::vector<sd::LongType> dimVec = {dimension};
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auto maxAlongDim = const_cast<NDArray&>(input).reduceAlongDimension(reduce::Max, &dimVec, true);
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auto minus = (input - *maxAlongDim);
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minus->applyTransform(transform::Exp, &output); // output contains exponents temporarily
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auto sumAlongDim = output.reduceAlongDimension(reduce::Sum, &dimVec, true);
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output /= *sumAlongDim;
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auto oneMinus = (1.f - output);
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output *= *oneMinus; // derivative
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delete sumAlongDim;
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delete minus;
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delete oneMinus;
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}
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}
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///////////////////////////////////////////////////////////////////
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template <typename T>
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void logSoftMaxForVector_(void const* input, sd::LongType const* inShapeInfo, void* output,
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sd::LongType const* outShapeInfo) {
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auto inBuff = reinterpret_cast<T const*>(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 = static_cast<T>(0);
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auto length = shape::length(inShapeInfo);
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sd::LongType inRank = shape::rank(inShapeInfo);
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sd::LongType *inShape = shape::shapeOf(inShapeInfo);
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sd::LongType *inStrides = shape::stride(inShapeInfo);
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sd::LongType *outShape = shape::shapeOf(outShapeInfo);
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sd::LongType *outStrides = shape::stride(outShapeInfo);
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sd::LongType outRank = shape::rank(outShapeInfo);
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sd::LongType inIndices[length];
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sd::LongType outIndices[length];
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PRAGMA_OMP_SIMD
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for (sd::LongType i2 = 0; i2 < length; i2++) {
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LongType coords[SD_MAX_RANK];
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sd::LongType idx2;
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INDEX2COORDS(i2,inRank, inShape, coords);
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COORDS2INDEX(inRank, inStrides, coords, idx2);
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max = sd::math::sd_max<T,T>(max, inBuff[idx2]);
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inIndices[i2] = idx2;
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}
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PRAGMA_OMP_SIMD
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for (sd::LongType i2 = 0; i2 < length; i2++) {
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LongType coords[SD_MAX_RANK];
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sd::LongType idx2;
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INDEX2COORDS(i2,outRank, outShape, coords);
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COORDS2INDEX(outRank, outStrides, coords, idx2);
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outBuff[idx2] = sd::math::sd_exp<T, T>(inBuff[inIndices[i2]] - max);
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sum += outBuff[idx2];
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}
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PRAGMA_OMP_SIMD
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for (sd::LongType i = 0; i < length; i++) {
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outBuff[outIndices[i]] /= sum;
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outBuff[outIndices[i]] = sd::math::sd_log<T, T>(outBuff[outIndices[i]]);
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}
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}
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///////////////////////////////////////////////////////////////////
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void logSoftMaxForVector(sd::LaunchContext* context, NDArray& input, NDArray& output) {
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if (!input.isVector() || !output.isVector())
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THROW_EXCEPTION("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_,
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(input.buffer(), input.shapeInfo(), output.buffer(), output.shapeInfo()), SD_FLOAT_TYPES);
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}
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//////////////////////////////////////////////////////////////////////////
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void prelu(LaunchContext* context, NDArray* input, NDArray* alpha, NDArray* output) {
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const sd::LongType inputLen = input->lengthOf();
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const sd::LongType* inputShapeInfo = input->shapeInfo();
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const sd::LongType* alphaShapeInfo = alpha->shapeInfo();
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auto func = PRAGMA_THREADS_FOR {
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for (sd::LongType i = start; i < stop; i++) {
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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(LaunchContext* context, NDArray* input, NDArray* alpha, NDArray* dLdO, NDArray* dLdI,
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NDArray* dLdA) {
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const sd::LongType inputLen = input->lengthOf();
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const sd::LongType* inputShapeInfo = input->shapeInfo();
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const sd::LongType* alphaShapeInfo = alpha->shapeInfo();
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float zero = 0.f;
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dLdA->assign(zero);
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for (sd::LongType 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->isScalar() ? dLdO->e<double>(0) : dLdO->e<double>(i);
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if (x < 0.0) {
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sd::LongType 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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} 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<sd::LongType> const& expectedShape, sd::LongType shapeLen) {
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sd::LongType expectedAlphaLen =
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std::accumulate(expectedShape.cbegin(), expectedShape.cend(), 1, std::multiplies<sd::LongType>());
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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 *input, double threshold, NDArray* output) {
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auto routine = LAMBDA_T(_x, threshold) { return _x > (T)threshold ? _x : (T)0.f; });
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input->applyLambda<T>(routine, output);
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}
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void thresholdRelu(LaunchContext* context, NDArray* input, double threshold, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), thresholdRelu_, (input, threshold, output), SD_FLOAT_TYPES);
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}
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template <typename T>
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static void thresholdReluDerivative_(sd::LaunchContext* context, NDArray* input, double theta, NDArray* dLdO,
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NDArray* output) {
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auto derivative = LAMBDA_TT(_x, grO, theta) {
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if (_x > theta)
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return grO;
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else
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return static_cast<T>(0);
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});
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input->applyPairwiseLambda<T>(dLdO, derivative, output);
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}
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void thresholdReluDerivative(sd::LaunchContext* context, NDArray* input, double threshold, NDArray* dLdO,
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NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), thresholdReluDerivative_, (context, input, threshold, dLdO, output),
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SD_FLOAT_TYPES);
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}
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///////////////////////////////////////////////////////////////////
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void logSoftmax(LaunchContext* context, 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_,
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(input->buffer(), input->shapeInfo(), output->buffer(), output->shapeInfo()), SD_FLOAT_TYPES);
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} else
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*output = 0.;
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} else {
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std::vector<sd::LongType> dimVector = {dimension};
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auto maxAlongDim = input->reduceAlongDimension(reduce::Max, &dimVector, true);
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auto maxMinusDim = *input - *maxAlongDim;
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maxMinusDim->applyTransform(transform::Exp, output); // output contains exponents temporarily
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auto sumAlongDim = output->reduceAlongDimension(reduce::Sum, &dimVector, true);
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*output /= *sumAlongDim;
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output->applyTransform(transform::Log, output);
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delete maxAlongDim;
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delete maxMinusDim;
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delete sumAlongDim;
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}
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}
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BUILD_SINGLE_TEMPLATE( void thresholdReluDerivative_,
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(sd::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output),
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SD_FLOAT_TYPES);
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BUILD_SINGLE_TEMPLATE( void logSoftMaxForVector_,
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(void const* input, sd::LongType const* inShapeInfo, void* output,
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sd::LongType const* outShapeInfo),
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SD_FLOAT_TYPES);
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BUILD_SINGLE_TEMPLATE( void _softMaxDerivForVector,
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(sd::LaunchContext * context, const void* input, const sd::LongType* inShapeInfo, void* output),
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SD_FLOAT_TYPES);
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} // namespace helpers
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} // namespace ops
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} // namespace sd
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