407 lines
17 KiB
C++
407 lines
17 KiB
C++
/* ******************************************************************************
|
|
*
|
|
*
|
|
* 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.
|
|
*
|
|
* See the NOTICE file distributed with this work for additional
|
|
* information regarding copyright ownership.
|
|
* 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
|
|
******************************************************************************/
|
|
|
|
//
|
|
// implementation of operations for Simple Recurrent Unit: arXiv:1709.02755v2 [cs.CL] 12 Sep 2017
|
|
//
|
|
// @author Yurii Shyrma, created on 05.12.2017
|
|
//
|
|
|
|
#include<ops/declarable/helpers/sru.h>
|
|
#include <array/NDArrayFactory.h>
|
|
#include <helpers/MmulHelper.h>
|
|
#include <execution/Threads.h>
|
|
|
|
namespace sd {
|
|
namespace ops {
|
|
namespace helpers {
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
static FORCEINLINE NDArray activation(const NDArray& arr) {
|
|
|
|
// return (const_cast<NDArray<T>&>(arr)).template transform<simdOps::Tanh<T>>();
|
|
auto result = NDArray(&arr, false, arr.getContext());
|
|
(const_cast<NDArray&>(arr)).applyTransform(transform::Tanh, result);
|
|
return result;
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
static FORCEINLINE NDArray sigmoid(const NDArray& arr) {
|
|
return (const_cast<NDArray&>(arr)).transform(transform::Sigmoid);
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void sruCell(sd::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c) {
|
|
|
|
// x input [bS x inSize], bS - batch size, inSize - number of features
|
|
// c0 previous cell state c [bS x inSize], that is at previous time step t-1
|
|
// w weights [inSize x 3*inSize]
|
|
// b biases [2*inSize]
|
|
|
|
// h current cell output [bS x inSize], that is at current time step t
|
|
// c current cell state [bS x inSize], that is at current time step t
|
|
|
|
const int inSize = x->sizeAt(1); // inSize - number of features
|
|
|
|
auto z = mmul(*x, *w); // [bS x 3*inSize]
|
|
|
|
// forget gate = sigmoid(x*Wf + bf)
|
|
auto f = sigmoid(z({0,0, inSize, 2*inSize}) + (*b)({0, inSize}));
|
|
|
|
// reset gate = sigmoid(x*Wr + br)
|
|
auto r = sigmoid(z({0,0, 2*inSize, 3*inSize}) + (*b)({inSize, 2*inSize}));
|
|
|
|
// ◦ means element-wise product or so called Hadamard product
|
|
// current sell state = f◦c0 + (1 - f)◦(x*Wc)
|
|
c->assign(f * (*c0) + (1.f - f) * z({0, 0 ,0, inSize}) );
|
|
// *c = f*(*c0 - z({},{0, inSize})) + z({{},{0, inSize}});
|
|
|
|
// current cell output = r◦activation(c) + (1 - r)◦x
|
|
h->assign( r * activation(*c) + (1.f - r) * (*x) );
|
|
// *h = r * (activation<T>(c) - *x) + *x;
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
void sruTimeLoop(sd::LaunchContext * context, const NDArray* x, const NDArray* c0, const NDArray* w, const NDArray* b, NDArray* h, NDArray* c) {
|
|
|
|
// x input [bS x inSize x time]
|
|
// c0 initial cell state (at time step = 0) [bS x inSize],
|
|
// w weights, [3*inSize x inSize]
|
|
// b biases, [2*inSize]
|
|
|
|
// h cell outputs [bS x inSize x time]
|
|
// c cell states [bS x inSize x time]
|
|
|
|
auto wT = w->transpose(); // [3*inSize x inSize] -> [inSize x 3*inSize]
|
|
|
|
const int time = x->sizeAt(2);
|
|
|
|
NDArray ct_1(*c0);
|
|
|
|
// loop through time steps
|
|
for (int t = 0; t < time; ++t) {
|
|
|
|
auto xt = (*x)({0,0, 0,0, t,t+1});
|
|
auto ht = (*h)({0,0, 0,0, t,t+1});
|
|
auto ct = (*c)({0,0, 0,0, t,t+1});
|
|
|
|
helpers::sruCell(context, &xt, &ct_1, &wT, b, &ht, &ct);
|
|
ct_1.assign(ct);
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void sruBI_(NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* mask, NDArray* ht, NDArray* ct) {
|
|
|
|
// x input 3d tensor [time x bS x 2*K], time - number of time steps, bS - batch size, K - number of features
|
|
// w 2d tensor of weights [2*K x 6*K]
|
|
// b row of biases with twice length [4*K]
|
|
// c0 2d tensor of initial state [bS x 2*K] at time t=0
|
|
// mask optional, 2d tensor of dropout mask [bS x 2*K]
|
|
|
|
// ht [time x bS x 2*K]
|
|
// ct [time x bS x 2*K]
|
|
|
|
const Nd4jLong time = x->sizeAt(0); // time - number of time steps
|
|
const Nd4jLong bS = x->sizeAt(1); // bS - batch size
|
|
const Nd4jLong K = x->sizeAt(2) / 2; // K - number of features
|
|
|
|
// x = x * mask
|
|
if(mask)
|
|
x->applyBroadcast(broadcast::Multiply, {1, 2}, *mask, *x); // apply mask
|
|
|
|
// U = x * w
|
|
NDArray wi = mmul(*x, *w); // U [time x bS x 6*K]
|
|
|
|
const Nd4jLong d2 = 2*K;
|
|
const Nd4jLong ncols = bS*d2;
|
|
const Nd4jLong ncolsWi = 3*ncols;
|
|
|
|
T* pI = x->bufferAsT<T>();
|
|
T* pWi = wi.bufferAsT<T>();
|
|
T* pBias = const_cast<NDArray*>(b)->bufferAsT<T>();
|
|
T* pInit = const_cast<NDArray*>(c0)->bufferAsT<T>();
|
|
T* pMask = mask ? const_cast<NDArray*>(mask)->bufferAsT<T>() : nullptr;
|
|
T* pHt = ht->bufferAsT<T>();
|
|
T* pCt = ct->bufferAsT<T>();
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto col = start; col < stop; col++) {
|
|
const auto colNum = col % d2;
|
|
bool flip = colNum >= K;
|
|
T maskVal = mask ? *(pMask + col) : T(1);
|
|
T cur = *(pInit + col);
|
|
T bF = *(pBias + colNum);
|
|
T bR = *(pBias + colNum + d2);
|
|
T *pWiVal = pWi + 3 * col;
|
|
T *pIVal = pI + col;
|
|
T *pHtVal = pHt + col;
|
|
T *pCtVal = pCt + col;
|
|
|
|
if (flip) {
|
|
const auto step = (time - 1) * ncols;
|
|
pIVal += step;
|
|
pHtVal += step;
|
|
pCtVal += step;
|
|
pWiVal += (time - 1) * ncolsWi;
|
|
}
|
|
|
|
auto ncolsRev = flip ? -ncols : ncols;
|
|
auto ncolsWiRev = flip ? -ncolsWi : ncolsWi;
|
|
|
|
for (Nd4jLong t = 0; t < time; ++t) {
|
|
// evaluate sigmoids
|
|
T ft = (1.) / (1. + sd::math::nd4j_exp<T, T>(-(pWiVal[1] + bF)));
|
|
T rt = (1.) / (1. + sd::math::nd4j_exp<T, T>(-(pWiVal[2] + bR)));
|
|
|
|
cur = (cur - *pWiVal) * ft + *pWiVal;
|
|
*pCtVal = cur;
|
|
T val = sd::math::nd4j_tanh<T, T>(cur);
|
|
*pHtVal = (val * maskVal - *pIVal) * rt + *pIVal;
|
|
|
|
pIVal += ncolsRev;
|
|
pWiVal += ncolsWiRev;
|
|
pCtVal += ncolsRev;
|
|
pHtVal += ncolsRev;
|
|
}
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_tad(func, 0, ncols);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void sruBIBP_(NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* ct, const NDArray* inGradC0, const NDArray* inGradHt, const NDArray* mask,
|
|
NDArray* gradI, NDArray* gradW, NDArray* gradB, NDArray* gradC0) {
|
|
|
|
// x input 3d tensor [time x bS x 2*K], time - number of time steps, bS - batch size, K - number of features
|
|
// w 2d tensor of weights [2*K x 6*K]
|
|
// b row of biases with twice length 4*K]
|
|
// c0 2d tensor of initial state [bS x 2*K] at time t=0
|
|
// ct [time x bS x 2*K]
|
|
// inGradC0 [bS x 2*K]
|
|
// inGradHt [time x bS x 2*K]
|
|
// mask optional, 2d tensor of dropout mask [bS x 2*K]
|
|
|
|
// gradI [time x bS x 2*K]
|
|
// gradW [time x 2*K x 6*K]
|
|
// gradB [4*K]
|
|
// gradC0 [bS x 2*K]
|
|
|
|
const Nd4jLong time = x->sizeAt(0); // time - number of time steps
|
|
const Nd4jLong bS = x->sizeAt(1);
|
|
const Nd4jLong K = x->sizeAt(2) / 2;
|
|
|
|
// x = x * mask
|
|
if(mask)
|
|
x->applyBroadcast(broadcast::Multiply, {1, 2}, *mask, *x); // apply mask
|
|
|
|
// U = x * w
|
|
NDArray wi = mmul(*x, *w); // [time x bS x 2*K] * [2*K x 6*K] = [time x bS x 6*K]
|
|
NDArray gradBias(x->ordering(), {bS, 4*K}, x->dataType(), x->getContext());
|
|
NDArray gradWi (x->ordering(), {time, bS, 6*K}, x->dataType(), x->getContext());
|
|
|
|
const Nd4jLong d2 = 2*K;
|
|
const Nd4jLong ncols = bS*d2;
|
|
const Nd4jLong ncolsWi = 3*ncols;
|
|
T* pInput = x->bufferAsT<T>();
|
|
T* pWi = wi.bufferAsT<T>();
|
|
T* pBias = const_cast<NDArray*>(b)->bufferAsT<T>();
|
|
T* pInit = const_cast<NDArray*>(c0)->bufferAsT<T>();
|
|
T* pMask = mask ? const_cast<NDArray*>(mask)->bufferAsT<T>() : nullptr;
|
|
T* pState = const_cast<NDArray*>(ct)->bufferAsT<T>();
|
|
T* pInGradCt = const_cast<NDArray*>(inGradC0)->bufferAsT<T>();
|
|
T* pInGradHt = const_cast<NDArray*>(inGradHt)->bufferAsT<T>();
|
|
T* pGradWi = gradWi.bufferAsT<T>();
|
|
T* pGradInput = gradI->bufferAsT<T>();
|
|
T* pGradBias = gradBias.bufferAsT<T>();
|
|
T* pGradInit = gradC0->bufferAsT<T>();
|
|
|
|
auto func = PRAGMA_THREADS_FOR {
|
|
for (auto col = start; col < stop; col++) {
|
|
T gbF = 0.f;
|
|
T gbR = 0.f;
|
|
const auto colNum = col % d2;
|
|
const bool flip = colNum >= K;
|
|
T maskVal = mask ? *(pMask + col) : T(1.);
|
|
T cur = *(pInGradCt + col);
|
|
T bF = *(pBias + colNum);
|
|
T bR = *(pBias + colNum + d2);
|
|
T *pWiVal = pWi + 3 * col;
|
|
T *pInputVal = pInput + col;
|
|
T *pStateVal = pState + col;
|
|
T *pInGradHtVal = pInGradHt + col;
|
|
T *pGradWiVal = pGradWi + 3 * col;
|
|
T *pGradInputVal = pGradInput + col;
|
|
|
|
if (!flip) {
|
|
const auto stepI = (time - 1) * ncols;
|
|
const auto stepW = (time - 1) * ncolsWi;
|
|
pInputVal += stepI;
|
|
pStateVal += stepI;
|
|
pInGradHtVal += stepI;
|
|
pGradInputVal += stepI;
|
|
pWiVal += stepW;
|
|
pGradWiVal += stepW;
|
|
}
|
|
|
|
Nd4jLong ncolsRev = flip ? -ncols : ncols;
|
|
Nd4jLong ncolsWiRev = flip ? -ncolsWi : ncolsWi;
|
|
|
|
for (Nd4jLong t = 0; t < time; ++t) {
|
|
// evaluate sigmoids
|
|
T ft = ((T) 1.) / ((T) 1. + sd::math::nd4j_exp<T, T>(-(*(pWiVal + 1) + bF)));
|
|
T rt = ((T) 1.) / ((T) 1. + sd::math::nd4j_exp<T, T>(-(*(pWiVal + 2) + bR)));
|
|
|
|
T val = sd::math::nd4j_tanh<T, T>(*pStateVal);
|
|
T prevVal = (t < time - 1) ? (*(pStateVal - ncolsRev)) : (*(pInit + col));
|
|
// grad wrt input
|
|
*pGradInputVal = *pInGradHtVal - (*pInGradHtVal) * rt;
|
|
// grad wrt rt, wiR and bR
|
|
T grt = (*pInGradHtVal) * (val * maskVal - *pInputVal) * (rt - rt * rt);
|
|
*(pGradWiVal + 2) = grt;
|
|
gbR += grt;
|
|
// grad wrt state
|
|
T gradSateVal = (*pInGradHtVal) * maskVal * (rt - rt * val * val) + cur;
|
|
// grad wrt wi0
|
|
*pGradWiVal = gradSateVal - gradSateVal * ft;
|
|
// grad wrt ft, wi1, and bF
|
|
T gft = gradSateVal * (prevVal - *pWiVal) * (ft - ft * ft);
|
|
*(pGradWiVal + 1) = gft;
|
|
gbF += gft;
|
|
// grad wrt c_previous
|
|
cur = gradSateVal * ft;
|
|
|
|
pInputVal -= ncolsRev;
|
|
pWiVal -= ncolsWiRev;
|
|
pStateVal -= ncolsRev;
|
|
pGradWiVal -= ncolsWiRev;
|
|
pGradInputVal -= ncolsRev;
|
|
pInGradHtVal -= ncolsRev;
|
|
}
|
|
*(pGradBias + col) = gbF;
|
|
*(pGradBias + col + ncols) = gbR;
|
|
*(pGradInit + col) = cur;
|
|
}
|
|
};
|
|
|
|
samediff::Threads::parallel_tad(func, 0, ncols);
|
|
|
|
// gradB
|
|
gradBias.reduceAlongDimension(reduce::Sum, *gradB, {0}); // [4*K]
|
|
|
|
// gradW
|
|
x->permutei({0, 2, 1}); // [time x bS x 2*K] -> [time x 2*K x bS]
|
|
MmulHelper::mmul(x, &gradWi, gradW, 1., 0.); // [time x 2*K x bS ] * [time x bS x 6*K] = [time x 2*K x 6*K]
|
|
}
|
|
|
|
|
|
void sruBI(sd::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* mask, NDArray* ht, NDArray* ct) {
|
|
BUILD_SINGLE_SELECTOR(x->dataType(), sruBI_, (x, w, b, c0, mask, ht, ct), FLOAT_TYPES);
|
|
}
|
|
void sruBIBP(sd::LaunchContext * context, NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* ct, const NDArray* inGradC0, const NDArray* inGradH, const NDArray* mask, NDArray* gradI, NDArray* gradW, NDArray* gradB, NDArray* gradC0) {
|
|
BUILD_SINGLE_SELECTOR(x->dataType(), sruBIBP_, (x, w, b, c0, ct, inGradC0, inGradH, mask, gradI, gradW, gradB, gradC0), FLOAT_TYPES);
|
|
}
|
|
|
|
|
|
BUILD_SINGLE_TEMPLATE(template void sruBI_, (NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* mask, NDArray* ht, NDArray* ct), FLOAT_TYPES);
|
|
BUILD_SINGLE_TEMPLATE(template void sruBIBP_, (NDArray* x, const NDArray* w, const NDArray* b, const NDArray* c0, const NDArray* ct, const NDArray* inGradC0, const NDArray* inGradH, const NDArray* mask, NDArray* gradI, NDArray* gradW, NDArray* gradB, NDArray* gradC0), FLOAT_TYPES);
|
|
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
// template <typename T>
|
|
// void sruCellBP(const std::vector<NDArray<T>*>& inArrs, const std::vector<NDArray<T>*>& outArrs) {
|
|
|
|
// NDArray<T>* x = inArrs[0]; // input [bS x inSize], bS - batch size, inSize - number of features
|
|
// NDArray<T>* c0 = inArrs[1]; // previous cell state c [bS x inSize], that is at previous time step t-1
|
|
// NDArray<T>* w = inArrs[2]; // weights [inSize x 3*inSize]
|
|
// NDArray<T>* b = inArrs[3]; // biases [2*inSize]
|
|
// NDArray<T>* dLdC = inArrs[4]; // gradient of the loss func with respect to cell output [bS x inSize]
|
|
// NDArray<T>* dLdH = inArrs[5]; // gradient of the loss func with respect to cell state [bS x inSize]
|
|
|
|
// NDArray<T>* dLdX = outArrs[0]; // gradient of the loss func with respect to input [bS x inSize], so called epsilon
|
|
// NDArray<T>* dLdW = outArrs[1]; // gradient of the loss func with respect to weights [inSize x 3*inSize]
|
|
// NDArray<T>* dLdB = outArrs[2]; // gradient of the loss func with respect to biases [2*inSize]
|
|
// NDArray<T>* dLdC0 = outArrs[3]; // gradient of the loss func with respect to previous cell state [bS, inSize]
|
|
|
|
// const int inSize = x->sizeAt(1); // inSize - number of features
|
|
|
|
// //*********** feed forward ***********//
|
|
// NDArray<T> z = mmul(*x, *w); // [bS x 3*inSize]
|
|
|
|
// // forget gate = sigmoid(x*Wf + bf)
|
|
// NDArray<T> f = sigmoid<T>(z({{},{inSize, 2*inSize}}) + (*b)({{0, inSize}})); // [bS, inSize]
|
|
// NDArray<T> oneMinusF = 1. - f;
|
|
|
|
// // reset gate = sigmoid(x*Wr + br)
|
|
// NDArray<T> r = sigmoid<T>(z({{},{2*inSize, 3*inSize}}) + (*b)({{inSize, 2*inSize}})); // [bS, inSize]
|
|
// NDArray<T> oneMinusR = 1. - r;
|
|
|
|
// // current sell state = f◦c0 + (1 - f)◦(x*Wc) ---> c->assign( f*(*c0) + ((T)1. - f) * z({{},{0, inSize}}) );
|
|
// // current cell output = r◦activation(c) + (1 - r)◦x ---> h->assign( r*activation<T>(*c) + ((T)1. - r) * (*x) );
|
|
|
|
|
|
// //*********** back propagation ***********//
|
|
// // dCdC0 = f;
|
|
// // dFdX = Wf
|
|
// // dRdX = Wr
|
|
|
|
// NDArray<T> tanh = activation<T>(*c);
|
|
// NDArray<T> dFdBf = f * oneMinusF;
|
|
// NDArray<T> dRdBr = r * oneMinusR;
|
|
// NDArray<T> dHdR = tanh - *x;
|
|
// // dCdF = c0 - x*Wc;
|
|
// NDArray<T> dCdF = *c0 - z({{},{0, inSize}});
|
|
// // dHdC = r * (1 - tanh*tanh)
|
|
// NDArray<T> dHdC = r * (1. - tanh * tanh);
|
|
// // dCdX = dCdX + dCdF*dFdX = (1-f)*Wc + dCdF*Wf
|
|
// NDArray<T> dCdX = oneMinusF * (*w)({{},{0, inSize}}) + dCdF * (*w)({{},{inSize, 2*inSize}});
|
|
|
|
|
|
// // dLdC0 = dLdC * dCdC0 = dLdC * f
|
|
// dLdC0->assign((*dLdC) * f);
|
|
|
|
|
|
// // dLdBf = dLdH*dHdBf + dLdC*dCdBf = dLdH*dHdC*dCdBf + dLdC*dCdF*dFdBf = dLdH*dHdC*dCdF*dFdBf + dLdC*dCdF*dFdBf = (dLdH*dHdC + dLdC)*dCdF*dFdBf
|
|
// (*dLdB)({{0, inSize}}).assign(((*dLdH) * dHdC + *dLdC) * dCdF * dFdBf);
|
|
// // dLdBr = dLdH * dHdR * dRdBr
|
|
// (*dLdB)({{inSize, 2*inSize}}).assign((*dLdH) * dHdR * dRdBr)
|
|
|
|
|
|
// // dLdWc = dLdH*dHdWc + dLdC*dCdWc = dLdH*dHdC*dCdWc + dLdC*dCdWc = (dLdH*dHdC + dLdC) * dCdWc = (dLdH*dHdC + dLdC) * (1-f)*x
|
|
// (*dLdW)({{}, {0, inSize}}).assign(((*dLdH) * dHdC + *dLdC) * oneMinusF * (*x));
|
|
// // dLdWf = dLdBf * x
|
|
// (*dLdW)({{}, {inSize, 2*inSize}}).assign((*dLdB)({{0, inSize}}) * (*x));
|
|
// // dLdWr = dLdBr * x
|
|
// (*dLdW)({{}, {2*inSize, 3*inSize}}).assign((*dLdB)({{inSize, 2*inSize}}) * (*x));
|
|
|
|
|
|
// // dLdX = dLdH*dHdX + dLdC*dCdX = dLdH*(dHdX + dHdR*dRdX + dHdC*dCdX) + dLdC*dCdF*dFdX = dLdH*(1 - r + dHdR*dRdX + dHdC*dCdX) + dLdC*dCdX
|
|
// dLdX->assign((*dLdH) * (oneMinusR + dHdR * (*w)({{},{2*inSize, 3*inSize}}) + dHdC * dCdX) + (*dLdC) * dCdX);
|
|
// }
|