359 lines
16 KiB
C++
359 lines
16 KiB
C++
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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 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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namespace nd4j {
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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 reluDerivative__(NDArray* theFirst, NDArray* theSecond) {
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auto functor = LAMBDA_TT(x, y){
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return x > (T) 0.f ? y : T(0.f);
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};
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theFirst->applyPairwiseLambda<T>(theSecond, functor, nullptr);
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}
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BUILD_SINGLE_TEMPLATE(template void reluDerivative__, (NDArray* input, NDArray* epsilon), FLOAT_TYPES);
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void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), reluDerivative__, (theFirst, theSecond), FLOAT_TYPES);
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}
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template <typename T>
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static void reluDerivative_(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 : T(0.f);
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};
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input->applyPairwiseLambda<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void reluDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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void reluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), reluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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template <typename T>
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static void relu6Derivative_(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 && x < (T)6.f? y : T(0.f);
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};
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input->applyPairwiseLambda<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void relu6Derivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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void relu6Derivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), relu6Derivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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template <typename T>
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static void leakyReluDerivative_(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? T(1.f) : T(0.f);
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};
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input->applyPairwiseLambda<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void leakyReluDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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void leakyReluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), leakyReluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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template <typename T>
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static void eluDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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return y * nd4j::math::nd4j_eluderivative<T,T>(x);
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};
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input->applyPairwiseLambda<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void eluDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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void eluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), eluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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template <typename T>
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static void seluDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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return y * simdOps::SELUDerivative<T>::op(x, nullptr);
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};
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input->applyPairwiseLambda<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void seluDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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void seluDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), seluDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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template <typename T>
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static 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<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void cubeDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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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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//return (x >= X(0.f) ? y: -y);
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template <typename T>
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static 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<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void reduceNorm1_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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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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template <typename T>
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static 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<T>(labels, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void sigmCrossEntropy_, (NDArray* logits, NDArray* labels, NDArray* output);, FLOAT_TYPES);
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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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template <typename T>
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static 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<T>(labels, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void sigmCrossEntropyGrad_, (NDArray* logits, NDArray* labels, NDArray*output);, FLOAT_TYPES);
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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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template <typename T>
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static void tanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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T th = nd4j::math::nd4j_tanh<T,T>(x);
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return y * ((T)1.0f - (th * th));
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};
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input->applyPairwiseLambda<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void tanhDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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void tanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), tanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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// return static_cast<X>(d2) * simdOps::HardTanhDerivative<X>::op(d1, nullptr);
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template <typename T>
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static void hardTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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T th = nd4j::math::nd4j_tanh<T,T>(x);
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return y * simdOps::HardTanhDerivative<T>::op(x, nullptr);
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};
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input->applyPairwiseLambda<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void hardTanhDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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void hardTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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template <typename T>
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static void rationalTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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return y * simdOps::RationalTanhDerivative<T>::op(x, nullptr);
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};
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input->applyPairwiseLambda<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void rationalTanhDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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void rationalTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), rationalTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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}
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template <typename T>
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static void rectifiedTanhDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
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auto functor = LAMBDA_TT(x, y){
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return x > (T) 0.0f ? y * (nd4j::math::nd4j_tanhderivative<T,T>(x)) : (T) 0.0f;
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};
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input->applyPairwiseLambda<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void rectifiedTanhDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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void rectifiedTanhDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
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BUILD_SINGLE_SELECTOR(theFirst->dataType(), rectifiedTanhDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
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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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template <typename T>
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static 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<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void softSignDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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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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template <typename T>
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static 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<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void softPlusDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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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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/// \param theFirst
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/// \param theSecond
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/// \param theOutput
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template <typename T>
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static 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<T>(epsilon, functor, output);
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}
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BUILD_SINGLE_TEMPLATE(template void sigmoidDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
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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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static 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<T>(epsilon, functor, output);
|
||
|
|
}
|
||
|
|
|
||
|
|
BUILD_SINGLE_TEMPLATE(template void hardSigmoidDerivative_, (NDArray* input, NDArray* epsilon, NDArray*output);, FLOAT_TYPES);
|
||
|
|
|
||
|
|
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);
|
||
|
|
}
|
||
|
|
BUILD_SINGLE_TEMPLATE(template void logSumExp_, (NDArray* input, NDArray* axis, NDArray*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);
|
||
|
|
}
|
||
|
|
BUILD_SINGLE_TEMPLATE(template void logSumExp_, (NDArray* input, NDArray* subtrah, NDArray* axis, NDArray*output);, FLOAT_TYPES);
|
||
|
|
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|