HardTanH.cpp

/* NiuTrans.Tensor - an open-source tensor library
 * Copyright (C) 2017, Natural Language Processing Lab, Northestern University. 
 * All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://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.
 */

/*
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-04-25
*/

#include <stdlib.h>
#include "HardTanH.h"
#include "HardTanH.cuh"

namespace nts{ // namespace nts(NiuTrans.Tensor)


/*
hard tanh function 
y =  1    if x > 1
     x    if -1 <= x <= 1
    -1    if x < -1
>> x - input tensor
>> y - result
*/
void HardTanH(XTensor * x, XTensor * y)
{
#ifdef USE_CUDA
    if(x->devID >= 0 || y->devID >= 0){
        CudaHardTanH(x, y);
        return;
    }
#endif
    if(x->dataType == DEFAULT_DTYPE && y->dataType == DEFAULT_DTYPE){
        int n = x->GetSize();
        DTYPE * ip = (DTYPE*)x->data;
        DTYPE * op = (DTYPE*)y->data;
        for(int i = 0; i < n; i++){
            DTYPE p = ip[i];
            if(p > 1.0)
                p = 1.0;
            else if(p < -1.0)
                p = -1.0;
            op[i] = p;
        }
    }
    else
        ShowNTErrors("TODO!");
}

/*
backward computation

dE/dx = dE/dy * dy/dx

hard tanh: y =  1    if x > 1
                x    if -1 <= x <= 1
               -1    if x< -1

   and dy/dx =  1    if -1 <= x <= 1
                0    otherwise

>> gold - gold standard to measure error (or loss)
>> y - output of the function
>> x - input of the function
>> dedy - dE/dy
>> dedx - dE/dx
>> lossName - type of loss function, e.g., cross entropy
*/
void HardTanHBackward(XTensor * gold, XTensor * y, XTensor * x, 
                      XTensor * dedy, XTensor * dedx,
                      LOSS_FUNCTION_NAME lossName)
{
    CheckNTErrors((gold == NULL || XTensor::IsIdentical(gold, y)), 
                        "The tensors must be of the same size!");

#ifdef USE_CUDA
    if(x->devID >= 0 || y->devID >= 0){
        CudaHardTanHBackward(gold, y, x, dedy, dedx, lossName);
        return;
    }
#endif

    if(x->dataType == DEFAULT_DTYPE && y->dataType == DEFAULT_DTYPE)
    {
        /* calculate dE/dy */
        if(lossName != NOLOSS)
            LossBackward(dedy, gold, y, lossName);

        DTYPE * dedyp = (DTYPE*)dedy->data;
        DTYPE * dedxp = (DTYPE*)dedx->data;
        DTYPE * ip = (DTYPE*)x->data;
        int size = y->unitNum;

        /* dE/dx = dE/dy * dy/dx */
        for(int i = 0; i < size; i++){
            DTYPE s =ip[i];
            if(s > 1.0 || s < -1.0)
                dedxp[i] = 0;
            else
                dedxp[i] = dedyp[i];
        }
    }
    else
        ShowNTErrors("TODO!");
}

} // namespace nts(NiuTrans.Tensor)