249 lines
12 KiB
Plaintext
249 lines
12 KiB
Plaintext
/*******************************************************************************
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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 GS <sgazeos@gmail.com>
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//
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#include <ops/declarable/helpers/legacy_helpers.h>
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#include <NDArrayFactory.h>
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#include <op_boilerplate.h>
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#include <ops/ops.h>
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namespace nd4j {
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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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linkage void cubeDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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return y * (3 * x * x);
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};
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input->applyPairwiseLambda(epsilon, functor, output);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void cubeDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), cubeDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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//return (x >= X(0.f) ? y: -y);
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template <typename T>
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linkage void reduceNorm1_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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return x > T(0.f)? y : -y;
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};
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input->applyPairwiseLambda(epsilon, functor, output);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void reduceNorm1(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), reduceNorm1_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////
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template <typename T>
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linkage void sigmCrossEntropy_(NDArray* logits, NDArray* labels, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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return nd4j::math::nd4j_max<T>(x, (T)0.f) - x * y + nd4j::math::nd4j_log<T,T>((T)1.f + nd4j::math::nd4j_exp<T,T>(-nd4j::math::nd4j_abs(x)));
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};
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logits->applyPairwiseLambda(labels, functor, output);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void sigmCrossEntropy(nd4j::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) {
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BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropy_, (logits, labels, output), FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////////////////////////////////////////////////
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template <typename T>
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linkage void sigmCrossEntropyGrad_(NDArray* logits, NDArray* labels, NDArray* output) {
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// 1 - labels - 1 / (1 + exp(logits))
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auto functor = LAMBDA_TT(x, y) {
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if(x <= 0)
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return static_cast<T>(1.) - y - static_cast<T>(1.) / (static_cast<T>(1.) + nd4j::math::nd4j_exp<T,T>(x));
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auto e = nd4j::math::nd4j_exp<T,T>(-x);
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return static_cast<T>(1.) - y - e / (static_cast<T>(1.) + e);
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};
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logits->applyPairwiseLambda(labels, functor, output);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void sigmCrossEntropyGrad(nd4j::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) {
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BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropyGrad_, (logits, labels, output), FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// X f = (X) 1.0f + nd4j::math::nd4j_abs<X>(d1);
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// return (X) d2 * ((X) 1.0f / (f * f));
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//
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template <typename T>
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linkage void softSignDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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T ss = (T)1.f + nd4j::math::nd4j_abs<T>(x);
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return y * ((T) 1.0f / (ss * ss));
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};
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input->applyPairwiseLambda(epsilon, functor, output);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void softSignDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), softSignDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename T>
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linkage void softPlusDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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T p = nd4j::math::nd4j_pow<T, T, T>(static_cast<T>(M_E), x);
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return y * (p / (p + 1.));
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};
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input->applyPairwiseLambda(epsilon, functor, output);
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}
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void softPlusDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), softPlusDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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///
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/// \param input
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/// \param epsilon
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/// \param output
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template <typename T>
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linkage void sigmoidDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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T s = nd4j::math::nd4j_sigmoid<T,T>(x);
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return y * (s * ((T) 1.0f - s));
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};
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input->applyPairwiseLambda(epsilon, functor, output);
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}
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void sigmoidDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), sigmoidDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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template <typename T>
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linkage void hardSigmoidDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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return y * simdOps::HardSigmoidDerivative<T>::op(x, nullptr);
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};
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input->applyPairwiseLambda(epsilon, functor, output);
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}
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void hardSigmoidDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardSigmoidDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename T>
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linkage void logSumExp_(NDArray* input, NDArray* axis, NDArray* output) {
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// reduce along axis with
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std::unique_ptr<NDArray> tempInput(input->dup());
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input->applyTransform(transform::Exp, tempInput.get());
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std::vector<int> axisVector;
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if (axis != nullptr) {
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axisVector.resize(axis->lengthOf());
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for (size_t i = 0; i < axisVector.size(); ++i)
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axisVector[i] = axis->e<int>(i);
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}
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tempInput->reduceAlongDimension(reduce::Sum, output, axisVector);
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output->applyTransform(transform::Log, nullptr, nullptr);
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}
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template <typename T>
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linkage void logSumExp_(NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) {
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// reduce along axis with
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std::unique_ptr<NDArray> tempInput(input->dup());
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input->applyPairwiseTransform(pairwise::Subtract, subtrah, tempInput.get());
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tempInput->applyTransform(transform::Exp, nullptr, nullptr);
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std::vector<int> axisVector;
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if (axis != nullptr) {
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axisVector.resize(axis->lengthOf());
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for (size_t i = 0; i < axisVector.size(); ++i)
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axisVector[i] = axis->e<int>(i);
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}
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tempInput->reduceAlongDimension(reduce::Sum, output, axisVector);
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output->applyTransform(transform::Log, nullptr, nullptr);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* axis, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, axis, output), FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, subtrah, axis, output), FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename T>
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void weightedCrossEntropyWithLogitsFunctor_(NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) {
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T posWeight = weights->e<T>(0);
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auto mainRoutineT1 = LAMBDA_TT(_x, _z, posWeight) {
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T targetWeight = (1. + (posWeight - (T)1.f) * _z);
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return (1. - _z) * _x +
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targetWeight * (nd4j::math::nd4j_log<T,T>((T)1.f + nd4j::math::nd4j_exp<T,T>(-nd4j::math::nd4j_abs(_x))) +
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nd4j::math::nd4j_max(-_x, T(0.f))
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);
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};
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auto mainRoutineT2 = LAMBDA_TTT(_x, _z, _w) {
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return (((T)1.0 - _z) * _x) +
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_w * (nd4j::math::nd4j_log<T,T>(T(1.) + nd4j::math::nd4j_exp<T,T>(-nd4j::math::nd4j_abs(_x))) +
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nd4j::math::nd4j_max(-_x, T(0.f)));
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};
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if (weights->isScalar()) {
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const_cast<NDArray*>(input)->applyPairwiseLambda(const_cast<NDArray*>(targets), mainRoutineT1, output);
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}
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else
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{
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std::unique_ptr<NDArray> targetVector(new NDArray(*weights));
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targetVector->applyScalar(scalar::Add, -1.f);
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std::unique_ptr<NDArray> targetTensor(new NDArray(*targets));
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*targetTensor = (*targetVector * *targetTensor) + T(1.f);
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const_cast<NDArray*>(input)->applyTriplewiseLambda(const_cast<NDArray*>(targets), targetTensor.get(), mainRoutineT2, output);
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void weightedCrossEntropyWithLogitsFunctor(nd4j::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) {
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NDArray::prepareSpecialUse({output}, {targets, input, weights});
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BUILD_SINGLE_SELECTOR(targets->dataType(), weightedCrossEntropyWithLogitsFunctor_, (targets, input, weights, output), FLOAT_TYPES);
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NDArray::registerSpecialUse({output}, {targets, input, weights});
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}
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}
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}
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} |