wehub-resource-sync
237 lines
10 KiB
Plaintext
237 lines
10 KiB
Plaintext
/* ******************************************************************************
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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 GS <sgazeos@gmail.com>
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//
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#include <array/NDArrayFactory.h>
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#include <ops/declarable/helpers/legacy_helpers.h>
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#include <ops/ops.h>
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#include <system/op_boilerplate.h>
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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 cubeDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y) { return y * (3 * x * x); });
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input->applyPairwiseLambda(epsilon, functor, output);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void cubeDerivative(LaunchContext* context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), cubeDerivative_, (theFirst, theSecond, theOutput), SD_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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void reduceNorm1_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y) { return x > T(0.f) ? y : -y; });
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input->applyPairwiseLambda(epsilon, functor, output);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void reduceNorm1(LaunchContext* context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), reduceNorm1_, (theFirst, theSecond, theOutput), SD_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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void sigmCrossEntropy_(NDArray* logits, NDArray* labels, NDArray* output) {
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auto functor = LAMBDA_TT(x, y) {
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return math::sd_max<T>(x, (T)0.f) - x * y + math::sd_log<T, T>((T)1.f + math::sd_exp<T, T>(-math::sd_abs<T,T>(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(LaunchContext* context, NDArray* logits, NDArray* labels, NDArray* output) {
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BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropy_, (logits, labels, output), SD_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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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) return static_cast<T>(1.) - y - static_cast<T>(1.) / (static_cast<T>(1.) + math::sd_exp<T, T>(x));
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auto e = math::sd_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(LaunchContext* context, NDArray* logits, NDArray* labels, NDArray* output) {
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BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropyGrad_, (logits, labels, output), SD_FLOAT_TYPES);
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}
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template <typename T>
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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 + math::sd_abs<T,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(LaunchContext* context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), softSignDerivative_, (theFirst, theSecond, theOutput), SD_FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename T>
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void softPlusDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y) {
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T p = math::sd_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(LaunchContext* context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), softPlusDerivative_, (theFirst, theSecond, theOutput), SD_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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void sigmoidDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y) {
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T s = math::sd_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(LaunchContext* context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), sigmoidDerivative_, (theFirst, theSecond, theOutput), SD_FLOAT_TYPES);
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}
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template <typename T>
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void hardSigmoidDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y) { return y * simdOps::HardSigmoidDerivative<T>::op(x, nullptr); });
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input->applyPairwiseLambda(epsilon, functor, output);
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}
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void hardSigmoidDerivative(LaunchContext* context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardSigmoidDerivative_, (theFirst, theSecond, theOutput), SD_FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename T>
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void logSumExp_(NDArray* input, NDArray* axis, NDArray* output) {
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// reduce along axis with
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NDArray *tempInput = input->dup();
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input->applyTransform(transform::Exp, tempInput);
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std::vector<LongType> 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) 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, output);
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}
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template <typename T>
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void logSumExp_(NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) {
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// reduce along axis with
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NDArray *tempInput = input->dup();
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input->applyPairwiseTransform(pairwise::Subtract, subtrah, &tempInput);
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tempInput.applyTransform(transform::Exp, &tempInput);
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std::vector<LongType> 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) 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, output);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void logSumExp(LaunchContext* context, NDArray* input, NDArray* axis, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, axis, output), SD_FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void logSumExp(LaunchContext* context, NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, subtrah, axis, output), SD_FLOAT_TYPES);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename T>
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void weightedCrossEntropyWithLogitsFunctor_(NDArray * targets, NDArray * input, NDArray * weights,
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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 * (math::sd_log<T, T>((T)1.f + math::sd_exp<T, T>(-math::sd_abs<T,T>(_x))) +
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math::sd_max(-_x, T(0.f)));
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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) + _w * (math::sd_log<T, T>(T(1.) + math::sd_exp<T, T>(-math::sd_abs<T,T>(_x))) + math::sd_max(-_x, T(0.f)));
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});
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if (weights->isScalar()) {
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input->applyPairwiseLambda(targets, mainRoutineT1, output);
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} else {
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std::unique_ptr<NDArray> targetVector(new NDArray(*weights));
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targetVector->applyScalar(scalar::Add, -1.f, targetVector.get());
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*targets = (*targetVector * *targets) + T(1.f);
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input->applyPairwiseLambda(targets, mainRoutineT1, output);
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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void weightedCrossEntropyWithLogitsFunctor(LaunchContext* context, NDArray * targets, NDArray * input,
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NDArray * 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),
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SD_FLOAT_TYPES);
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NDArray::registerSpecialUse({output}, {targets, input, weights});
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}
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} // namespace helpers
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} // namespace ops
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} // namespace sd
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