softmax as standalone compilation unit
Signed-off-by: raver119 <raver119@gmail.com>master
raver119
parent
4d81af9fe9
commit
ca96a13ed0
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@ -29,52 +29,6 @@ namespace sd {
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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 = sd::math::nd4j_max<T>(max, inBuff[i]);
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for (int i = 0; i < length; i++) {
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outBuff[i] = sd::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 = sd::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 = sd::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(sd::LaunchContext * context, const void *input, const Nd4jLong *inShapeInfo, void *output) {
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@ -123,16 +77,6 @@ static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output,
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}
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}
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///////////////////////////////////////////////////////////////////
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void softMaxForVector(sd::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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@ -191,148 +135,6 @@ void softMaxForVector(sd::LaunchContext * context, const NDArray& input, NDArray
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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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template <typename T>
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void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen);
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template <>
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FORCEINLINE void softmax_loop(float *input, float *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) {
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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 inBuff = input + offsets[i];
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auto outBuff = output + offsets[i];
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float max = -DataTypeUtils::max<float>();
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float sum = 0.f;
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#pragma omp simd reduction(max:max)
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for (uint j = 0; j < tadLen; ++j)
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max = sd::math::nd4j_max<float>(max, inBuff[j]);
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#pragma omp simd reduction(+:sum)
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for (uint j = 0; j < tadLen; ++j) {
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float temp = sd::math::nd4j_exp<float, float>(inBuff[j] - max);
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outBuff[j] = temp;
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sum += temp;
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}
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#pragma omp simd
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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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template <typename T>
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FORCEINLINE void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) {
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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 inBuff = input + offsets[i];
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auto outBuff = output + offsets[i];
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T max = -DataTypeUtils::max<T>();
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T sum(0.f);
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#pragma omp simd reduction(maxT:max)
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for (uint j = 0; j < tadLen; ++j)
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max = sd::math::nd4j_max<T>(max, inBuff[j]);
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#pragma omp simd reduction(sumT:sum)
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for (uint j = 0; j < tadLen; ++j) {
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T temp = sd::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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#pragma omp simd
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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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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void softmax_(sd::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 = sd::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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T *inBuff = input.bufferAsT<T>();
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T *outBuff = output.bufferAsT<T>();
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softmax_loop(inBuff, outBuff, tadOffsets, numOfSubArrs, tadLen);
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}
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else {
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uint inShapeInfoCast[MAX_RANK];
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bool canCast = sd::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++) {
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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 = sd::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 = sd::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.reduceAlongDimension(sd::reduce::Max, {dimension}, true);
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input.applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), max, output, false);
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output.applyTransform(sd::transform::Exp, output);
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NDArray sum = output.reduceAlongDimension(sd::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(sd::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(sd::LaunchContext * context, const NDArray& input, const NDArray& alpha, NDArray& output) {
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@ -433,7 +235,6 @@ void preluBP(sd::LaunchContext * context, const NDArray& input, const NDArray& a
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}
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BUILD_SINGLE_TEMPLATE(template void thresholdReluDerivative_, (sd::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output), FLOAT_TYPES);
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BUILD_SINGLE_TEMPLATE(template void softmax_, (sd::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, (sd::LaunchContext * context, const void *input, const Nd4jLong *inShapeInfo, void *output), FLOAT_TYPES);
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@ -0,0 +1,228 @@
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/*******************************************************************************
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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 <helpers/ShapeUtils.h>
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#include <numeric>
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#include <helpers/ConstantTadHelper.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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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 = sd::math::nd4j_max<T>(max, inBuff[i]);
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for (int i = 0; i < length; i++) {
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outBuff[i] = sd::math::nd4j_exp<T, T>(inBuff[i] - max);
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sum += outBuff[i];
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}
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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 = sd::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 = sd::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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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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void softMaxForVector(sd::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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template <typename T>
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void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen);
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template <>
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FORCEINLINE void softmax_loop(float *input, float *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) {
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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 inBuff = input + offsets[i];
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auto outBuff = output + offsets[i];
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float max = -DataTypeUtils::max<float>();
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float sum = 0.f;
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#pragma omp simd reduction(max:max)
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for (uint j = 0; j < tadLen; ++j)
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max = sd::math::nd4j_max<float>(max, inBuff[j]);
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#pragma omp simd reduction(+:sum)
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for (uint j = 0; j < tadLen; ++j) {
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float temp = sd::math::nd4j_exp<float, float>(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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template <typename T>
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FORCEINLINE void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) {
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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 inBuff = input + offsets[i];
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auto outBuff = output + offsets[i];
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T max = -DataTypeUtils::max<T>();
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T sum(0.f);
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#pragma omp simd reduction(maxT:max)
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for (uint j = 0; j < tadLen; ++j)
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max = sd::math::nd4j_max<T>(max, inBuff[j]);
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#pragma omp simd reduction(sumT:sum)
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for (uint j = 0; j < tadLen; ++j) {
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T temp = sd::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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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void softmax_(sd::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 = sd::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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T *inBuff = input.bufferAsT<T>();
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T *outBuff = output.bufferAsT<T>();
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softmax_loop(inBuff, outBuff, tadOffsets, numOfSubArrs, tadLen);
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}
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else {
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uint inShapeInfoCast[MAX_RANK];
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bool canCast = sd::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++) {
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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 = sd::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 = sd::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.reduceAlongDimension(sd::reduce::Max, {dimension}, true);
|
||||
input.applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), max, output, false);
|
||||
output.applyTransform(sd::transform::Exp, output);
|
||||
NDArray sum = output.reduceAlongDimension(sd::reduce::Sum, {dimension}, true);
|
||||
output /= sum;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
void softmax(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
|
||||
|
||||
BUILD_SINGLE_SELECTOR(input.dataType(), softmax_, (context, input, output, dimension), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue