/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://www.apache.org/licenses/LICENSE-2.0. * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the * License for the specific language governing permissions and limitations * under the License. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author GS <sgazeos@gmail.com> // #include <ops/declarable/helpers/legacy_helpers.h> #include <NDArrayFactory.h> namespace nd4j { namespace ops { namespace helpers { template <typename T> static void reluDerivative__(NDArray* theFirst, NDArray* theSecond) { auto functor = LAMBDA_TT(x, y){ return x > (T) 0.f ? y : T(0.f); }; theFirst->applyPairwiseLambda<T>(theSecond, functor, nullptr); } void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), reluDerivative__, (theFirst, theSecond), FLOAT_TYPES); } template <typename T> static void reluDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { T zero = (T) 0.f; auto functor = LAMBDA_TT(x, y, zero){ return x > zero ? y : zero; }; input->applyPairwiseLambda<T>(epsilon, functor, output); /* auto x = input->bufferAsT<T>(); auto y = epsilon->bufferAsT<T>(); auto z = output->bufferAsT<T>(); int length = input->lengthOf(); T zero = (T) 0.f; PRAGMA_OMP_PARALLEL_FOR for (int e = 0; e < length; e++) { z[e] = x[e] > zero ? y[e] : zero; } */ } void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), reluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template <typename T> static void relu6Derivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ return x > (T)0.f && x < (T)6.f? y : T(0.f); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void relu6Derivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), relu6Derivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template <typename T> static void leakyReluDerivative_(NDArray* input, NDArray* epsilon, NDArray* output, const float alpha) { const T alphaT = static_cast<T>(alpha); auto functor = LAMBDA_TT(x, y, alphaT) { return x < 0 ? alphaT * y : y; }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void leakyReluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), leakyReluDerivative_, (theFirst, theSecond, theOutput, alpha), FLOAT_TYPES); } template <typename T> static void eluDerivative_(NDArray* input, NDArray* epsilon, NDArray* output, const float alpha) { const T alphaT = static_cast<T>(alpha); auto functor = LAMBDA_TT(x, y, alphaT){ return y * nd4j::math::nd4j_eluderivative<T,T>(x, alphaT); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void eluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput, const float alpha) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), eluDerivative_, (theFirst, theSecond, theOutput, alpha), FLOAT_TYPES); } template <typename T> static void seluDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ return y * simdOps::SELUDerivative<T>::op(x, nullptr); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void seluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), seluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template <typename T> static void cubeDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ return y * (3 * x * x); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void cubeDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), cubeDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } //return (x >= X(0.f) ? y: -y); template <typename T> static void reduceNorm1_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ return x > T(0.f)? y : -y; }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void reduceNorm1(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), reduceNorm1_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } //////////////////////////////////////////////////////////////////////// template <typename T> static void sigmCrossEntropy_(NDArray* logits, NDArray* labels, NDArray* output) { auto functor = LAMBDA_TT(x, y){ 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))); }; logits->applyPairwiseLambda<T>(labels, functor, output); } void sigmCrossEntropy(nd4j::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) { BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropy_, (logits, labels, output), FLOAT_TYPES); } //////////////////////////////////////////////////////////////////////// template <typename T> static void sigmCrossEntropyGrad_(NDArray* logits, NDArray* labels, NDArray* output) { // 1 - labels - 1 / (1 + exp(logits)) auto functor = LAMBDA_TT(x, y) { if(x <= 0) return static_cast<T>(1.) - y - static_cast<T>(1.) / (static_cast<T>(1.) + nd4j::math::nd4j_exp<T,T>(x)); auto e = nd4j::math::nd4j_exp<T,T>(-x); return static_cast<T>(1.) - y - e / (static_cast<T>(1.) + e); }; logits->applyPairwiseLambda<T>(labels, functor, output); } void sigmCrossEntropyGrad(nd4j::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) { BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropyGrad_, (logits, labels, output), FLOAT_TYPES); } //////////////////////////////////////////////////////////////////////// template <typename T> static void tanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ T th = nd4j::math::nd4j_tanh<T,T>(x); return y * ((T)1.0f - (th * th)); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void tanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), tanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } // return static_cast<X>(d2) * simdOps::HardTanhDerivative<X>::op(d1, nullptr); template <typename T> static void hardTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ T th = nd4j::math::nd4j_tanh<T,T>(x); return y * simdOps::HardTanhDerivative<T>::op(x, nullptr); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void hardTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template <typename T> static void rationalTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ return y * simdOps::RationalTanhDerivative<T>::op(x, nullptr); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void rationalTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), rationalTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template <typename T> static void rectifiedTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ return x > (T) 0.0f ? y * (nd4j::math::nd4j_tanhderivative<T,T>(x)) : (T) 0.0f; }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void rectifiedTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), rectifiedTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } // X f = (X) 1.0f + nd4j::math::nd4j_abs<X>(d1); // return (X) d2 * ((X) 1.0f / (f * f)); template <typename T> static void softSignDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ T ss = (T)1.f + nd4j::math::nd4j_abs<T>(x); return y * ((T) 1.0f / (ss * ss)); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void softSignDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), softSignDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template <typename T> static void softPlusDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ T p = nd4j::math::nd4j_pow<T, T, T>(static_cast<T>(M_E), x); return y * (p / (p + 1.)); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void softPlusDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), softPlusDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } /// /// \param theFirst /// \param theSecond /// \param theOutput template <typename T> static void sigmoidDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ T s = nd4j::math::nd4j_sigmoid<T,T>(x); return y * (s * ((T) 1.0f - s)); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void sigmoidDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), sigmoidDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template <typename T> static void hardSigmoidDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) { auto functor = LAMBDA_TT(x, y){ return y * simdOps::HardSigmoidDerivative<T>::op(x, nullptr); }; input->applyPairwiseLambda<T>(epsilon, functor, output); } void hardSigmoidDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) { BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardSigmoidDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES); } template <typename T> static void logSumExp_(NDArray* input, NDArray* axis, NDArray* output) { // reduce along axis with std::unique_ptr<NDArray> tempInput(input->dup()); input->applyTransform(transform::Exp, tempInput.get()); std::vector<int> axisVector; if (axis != nullptr) { axisVector.resize(axis->lengthOf()); for (size_t i = 0; i < axisVector.size(); ++i) axisVector[i] = axis->e<int>(i); } tempInput->reduceAlongDimension(reduce::Sum, output, axisVector); output->applyTransform(transform::Log, nullptr, nullptr); } template <typename T> static void logSumExp_(NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) { // reduce along axis with std::unique_ptr<NDArray> tempInput(input->dup()); input->applyPairwiseTransform(pairwise::Subtract, subtrah, tempInput.get(), nullptr); tempInput->applyTransform(transform::Exp, nullptr, nullptr); std::vector<int> axisVector; if (axis != nullptr) { axisVector.resize(axis->lengthOf()); for (size_t i = 0; i < axisVector.size(); ++i) axisVector[i] = axis->e<int>(i); } tempInput->reduceAlongDimension(reduce::Sum, output, axisVector); output->applyTransform(transform::Log, nullptr, nullptr); } void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* axis, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, axis, output), FLOAT_TYPES); } void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) { BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, subtrah, axis, output), FLOAT_TYPES); } ////////////////////////////////////////////////////////////////////////// template <typename T> static void weightedCrossEntropyWithLogitsFunctor_(NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) { T posWeight = weights->e<T>(0); auto mainRoutineT1 = LAMBDA_TT(_x, _z, posWeight) { T targetWeight = (1. + (posWeight - (T)1.f) * _z); return (1. - _z) * _x + targetWeight * (nd4j::math::nd4j_log<T,T>((T)1.f + nd4j::math::nd4j_exp<T,T>(-nd4j::math::nd4j_abs(_x))) + nd4j::math::nd4j_max(-_x, T(0.f)) ); }; auto mainRoutineT2 = LAMBDA_TTT(_x, _z, _w) { return (((T)1.0 - _z) * _x) + _w * (nd4j::math::nd4j_log<T,T>(T(1.) + nd4j::math::nd4j_exp<T,T>(-nd4j::math::nd4j_abs(_x))) + nd4j::math::nd4j_max(-_x, T(0.f))); }; if (weights->isScalar()) { const_cast<NDArray*>(input)->applyPairwiseLambda<T>(const_cast<NDArray*>(targets), mainRoutineT1, output); } else { std::unique_ptr<NDArray> targetVector(new NDArray(*weights)); targetVector->applyScalar(scalar::Add, -1.f); std::unique_ptr<NDArray> targetTensor(new NDArray(*targets)); *targetTensor = (*targetVector * *targetTensor) + T(1.f); const_cast<NDArray*>(input)->applyTriplewiseLambda<T>(const_cast<NDArray*>(targets), targetTensor.get(), mainRoutineT2, output); } } void weightedCrossEntropyWithLogitsFunctor(nd4j::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) { BUILD_SINGLE_SELECTOR(targets->dataType(), weightedCrossEntropyWithLogitsFunctor_, (targets, input, weights, output), FLOAT_TYPES); } } } }