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