314 lines
14 KiB
C++
314 lines
14 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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// @author Yurii Shyrma (iuriish@yahoo.com), created on 15.02.2018, Alex Black
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//
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// implementation of gated Recurrent Unit cell
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// (cf. http://arxiv.org/abs/1406.1078).
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// Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
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// "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation"
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#include<ops/declarable/helpers/gru.h>
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#include <ops/declarable/CustomOperations.h>
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#include<ops/declarable/helpers/transforms.h>
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#include <MmulHelper.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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void gruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* Wru, const NDArray* Wc,
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const NDArray* bru, const NDArray* bc,
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NDArray* r, NDArray* u, NDArray* c, NDArray* h) {
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//Inputs:
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// x input [bS, nIn], nIn - input size
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// hLast previous cell output [bS, nUn], that is at previous time step t-1, nUn - number of units
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// Wru RU weights - [nIn+nUn, 2*nUn] - reset and update gates
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// Wc C weights - [nIn+nUn, nUn] - cell gate
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// bru r and u biases, [2*nUn] - reset and update gates
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// bc c biases, [nUn] - cell gate
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//Outputs:
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// r Reset gate output [bS, nUn]
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// u Update gate output [bS, nUn]
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// c Cell gate output [bS, nUn]
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// h current cell output [bS, nUn]
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/***************************************************************************************/
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/************************ THIS IS NOT OPTIMAZED CODE ***********************************/
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/** however it is more math-friendly and convenient for backprop formulas derivation) **/
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const int bS = x->sizeAt(0);
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const int nIn = x->sizeAt(1);
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const int nUn = hLast->sizeAt(1);
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NDArray Wr = (*Wru)({0,nIn, 0,0}); // reset gates weights [nIn, 2*nUn]
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NDArray Wu = (*Wru)({nIn,nIn+nUn, 0,0}); // updates gates weights [nUn, 2*nUn]
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NDArray Wcr = (*Wc)({0,nIn, 0,0}); // reset cell weights [nIn, nUn]
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NDArray Wcu = (*Wc)({nIn,nIn+nUn, 0,0}); // updates cell weights [nUn, nUn]
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// gates = sigmoid(x*Wr + hLast*Wu + br + bu)
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NDArray gates = mmul(*x, Wr) + mmul(*hLast, Wu) + *bru; // [bS, nIn] * [nIn, 2*nUn] + [bS, nUn] * [nUn, 2*nUn] + [2*nUn] = [bS, 2*nUn]
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gates.applyTransform(transform::Sigmoid);
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// reset gate
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r->assign(gates({0,0, 0,nUn})); // [bS, nUn]
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// update gate
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u->assign(gates({0,0, nUn,2*nUn})); // [bS, nUn]
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// cell gate c = activation(x*Wcr + (r◦hlast)*Wcu + bc)
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c->assign(mmul(*x, Wcr) + mmul(*r * *hLast, Wcu) + *bc); // [bS, nIn] * [nIn, nUn] + [bS, nUn] * [nUn, nUn] + [nUn] = [bS, nUn]
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c->applyTransform(transform::Tanh);
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// cell output
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h->assign(*u * *hLast + (1.f - *u) * *c);
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/***************************************************************************************/
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/********************** THIS MORE OPTIMAZED CODE (except concat ) **********************/
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/***************************************************************************************/
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/*
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//Concat inputs: x + hLast : [bs, nIn + nUn]
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NDArray xhConcat(x->ordering(), {bS, nIn + nUn}, x->dataType(), context); // concat([bs, nIn], [bs, nUn]) -> [bs, nIn + nUn]
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helpers::concat(context, {const_cast<NDArray*>(x), const_cast<NDArray*>(hLast)}, xhConcat, {1});
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//mmul for reset and update gates: (x * weight_ux + hLast * weight_xr + b_u)
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auto m = mmul(xhConcat, *Wru) + *bru ; // [bs, nIn+nUn] * [nIn+nUn, 2*nUn] = [bs, 2*nUn]
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// m += *bru;
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sigmoidInplace(m); //sigmoid(rz) and sigmoid(uz)
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r->assign(m({0,0, 0, nUn}));
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u->assign(m({0,0, nUn, 2*nUn}));
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// hLast = hLast * r
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xhConcat({0,0, nIn, nIn+nUn}) *= *r;
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//c = tanh(x * weight_cx + (hLast .* r) * weight_cr + b_c)
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MmulHelper::mmul(&xhConcat, Wc, c, 1.0, 0.0); //c = 1.0 * xhConcat * Wc + 0.0 * c
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*c += *bc;
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tanhInplace(*c);
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//Output: h = (1-u).*c + u .* hPrev
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//auto hResult = (*u) * (*hLast) + (1.0f - *u) * (*c); const_cast<NDArray*>(h)->assign(&hResult);
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u->applyPairwiseTransform(pairwise::Multiply, hLast, h, nullptr); //h = u * hLast
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auto temp = (1.0f - *u);
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temp *= (*c);
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(*h) += temp;
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*/
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}
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//////////////////////////////////////////////////////////////////////////
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void gruTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* h0, const NDArray* Wx, const NDArray* Wh, const NDArray* b, NDArray* h) {
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// x input [time, bS, iS]
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// h0 initial cell output (at time step = 0) [bS, nUn]
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// Wx input-to-hidden weights, [iS, 3*nUn]
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// Wh hidden-to-hidden weights, [nUn, 3*nUn]
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// b biases, [3*nUn]
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// h is cell outputs at each time step [time, bS, nUn]
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const int time = x->sizeAt(0);
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NDArray ht_1(*h0);
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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)({t,t+1, 0,0, 0,0});
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auto ht = (*h)({t,t+1, 0,0, 0,0});
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//helpers::gruCell(&xt, &ht_1, Wx, Wh, b, &ht);
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//ht_1.assign(ht);
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}
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}
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//////////////////////////////////////////////////////////////////////////
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void gruCellBP(nd4j::LaunchContext * context, const NDArray* x, const NDArray* h0, const NDArray* Wx, const NDArray* Wh, const NDArray* b, const NDArray* dLdh, const NDArray* dLdWx0,
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const NDArray* dLdWh0, const NDArray* dLdb0, NDArray* dLdx, NDArray* dLdh0, NDArray* dLdWx, NDArray* dLdWh, NDArray* dLdb) {
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// x input [bS, iS]
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// h0 previous cell output [bS, nUn], that is at previous time step t-1
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// Wx input-to-hidden weights, [iS, 3*nUn]
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// Wh hidden-to-hidden weights, [nUn, 3*nUn]
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// b biases, [3*nUn]
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// dLdh gradient wrt output, [bS,nUn], that is epsilon_next
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// dLdWx0 gradient wrt Wx at previous time step, [iS, 3*nUn]
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// dLdWh0 gradient wrt Wh at previous time step, [nUn, 3*nUn]
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// dLdb0 gradient wrt b at previous time step, [3*nUn]
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// dLdx gradient wrt x, [bS, iS], that is epsilon
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// dLdh0 gradient wrt h0, [bS, nUn]
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// dLdWx gradient wrt Wx, [iS, 3*nUn]
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// dLdWh gradient wrt Wh, [nUn, 3*nUn]
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// dLdb gradient wrt b at previous time step, [3*nUn]
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// h is current cell output [bS, nUn], that is at current time step t
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const int nUn = h0->sizeAt(1);
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// ***** feed forward step ***** //
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// gates = sigmoid(x*Wx + h0*Wh + b)
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auto gates = sigmoid(mmul(*x, (*Wx)({0,0, 0,2*nUn})) + mmul(*h0, (*Wh)({0,0, 0,2*nUn})) + (*b)({0,2*nUn})); // [bS, 2*nUn] + [bS, 2*nUn] + [1, 2*nUn] = [bS, 2*nUn]
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// reset gate
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auto r = gates({0,0, 0, nUn}); // [bS, nUn]
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// update gate
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auto u = gates({0,0, nUn, 2*nUn}); // [bS, nUn]
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// ◦ means element-wise product or so called Hadamard product
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// n = tanh(x*Wx + (r◦h0)*Wh + b)
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auto n = tanh(mmul(*x, (*Wx)({0,0, 2*nUn,3*nUn})) + mmul((*h0)*r, (*Wh)({0,0, 2*nUn,3*nUn})) + (*b)({2*nUn,3*nUn})); // [bS, nUn]
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// ***** back prop step ***** //
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auto Wxr = (*Wx)({0,0, 0, nUn});
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auto Wxu = (*Wx)({0,0, nUn, 2*nUn});
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auto Wxn = (*Wx)({0,0, 2*nUn,3*nUn});
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auto Whr = (*Wh)({0,0, 0, nUn});
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auto Whu = (*Wh)({0,0, nUn, 2*nUn});
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auto Whn = (*Wh)({0,0, 2*nUn,3*nUn});
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auto WxrT = Wxr.transpose();
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auto WxuT = Wxu.transpose();
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auto WxnT = Wxn.transpose();
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auto WhrT = Whr.transpose();
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auto WhuT = Whu.transpose();
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auto WhnT = Whn.transpose();
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auto xT = x->transpose();
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auto h0T = h0->transpose();
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auto dLdWxr = (*dLdWx)({0,0, 0, nUn});
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auto dLdWxu = (*dLdWx)({0,0, nUn, 2*nUn});
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auto dLdWxn = (*dLdWx)({0,0, 2*nUn,3*nUn});
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auto dLdWhr = (*dLdWh)({0,0, 0, nUn});
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auto dLdWhu = (*dLdWh)({0,0, nUn, 2*nUn});
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auto dLdWhn = (*dLdWh)({0,0, 2*nUn,3*nUn});
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auto dLdbr = (*dLdb)({0, nUn});
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auto dLdbu = (*dLdb)({nUn, 2*nUn});
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auto dLdbn = (*dLdb)({2*nUn,3*nUn});
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auto dhdu = *h0 - n; // [bS, nUn]
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auto dhdn = 1.f - u; // [bS, nUn]
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auto dSigdu = u * (1.f - u); // [bS, nUn]
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auto dSigdr = r * (1.f - r); // [bS, nUn]
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auto dActdn = 1.f - n * n; // [bS, nUn]
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auto dndr = mmul(dActdn * (*h0), WhnT);
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auto drdh0 = mmul(dSigdr, WhrT);
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auto dLdn = (*dLdh) * dhdn;
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auto dLdu = (*dLdh) * dhdu;
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auto dLdr = dLdn * dndr;
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dLdx->assign( mmul(dLdu * dSigdu, WxuT) + mmul(dLdr * dSigdr, WxrT) + mmul(dLdn * dActdn, WxnT) ); // [bS,iS]
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dLdh0->assign( mmul(dLdu * dSigdu, WhuT) + mmul(dLdn * dActdn * (r + drdh0), WhnT) + (*dLdh)*u ); // [bS,nUn]
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dLdWxr.assign( mmul(xT, dSigdr * dLdr) ); // [iS,nUn]
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dLdWhr.assign( mmul(h0T, dSigdr * dLdr) ); // [nUn,nUn]
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dLdWxu.assign( mmul(xT, dSigdu * dLdu) ); // [iS,nUn]
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dLdWhu.assign( mmul(h0T, dSigdu * dLdu) ); // [nUn,nUn]
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dLdWxn.assign( mmul(xT, dActdn * dLdn) ); // [iS,nUn]
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dLdWhn.assign( mmul((r*(*h0)).transpose(), dActdn * dLdn) ); // [nUn,nUn]
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dLdbr.assign( (dSigdr * dLdr).reduceAlongDims(reduce::Sum, {0})); // [nUn]
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dLdbu.assign( (dSigdu * dLdu).reduceAlongDims(reduce::Sum, {0})); // [nUn]
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dLdbn.assign( (dActdn * dLdn).reduceAlongDims(reduce::Sum, {0})); // [nUn]
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if(dLdWx0 != nullptr)
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*dLdWx += *dLdWx0;
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if(dLdWh0 != nullptr)
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*dLdWh += *dLdWh0;
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if(dLdb0 != nullptr)
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*dLdb += *dLdb0;
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}
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// //////////////////////////////////////////////////////////////////////////
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// FIXME - gruTimeLoopBP is not correct
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// template <typename T>
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// void gruTimeLoopBP(const std::vector<NDArray<T>*>& inArrs, const std::vector<NDArray<T>*>& outArrs) {
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// NDArray<T>* x = inArrs[0]; // input [time, bS, iS]
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// NDArray<T>* hi = inArrs[1]; // previous/initial cell output [bS, nUn], that is at previous time step t-1
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// NDArray<T>* Wx = inArrs[2]; // input-to-hidden weights, [iS, 3*nUn]
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// NDArray<T>* Wh = inArrs[3]; // hidden-to-hidden weights, [nUn, 3*nUn]
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// NDArray<T>* b = inArrs[4]; // biases, [3*nUn]
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// NDArray<T>* dLdh = inArrs[5]; // gradient wrt output, [time, bS, nUn], that is epsilon_next
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// NDArray<T>* dLdx = outArrs[0]; // gradient wrt x, [time, bS, iS], that is epsilon
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// NDArray<T>* dLdhi = outArrs[1]; // gradient wrt hi, [bS, nUn]
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// NDArray<T>* dLdWx = outArrs[2]; // gradient wrt Wx, [iS, 3*nUn]
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// NDArray<T>* dLdWh = outArrs[3]; // gradient wrt Wh, [nUn, 3*nUn]
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// NDArray<T>* dLdb = outArrs[4]; // gradient wrt b, [3*nUn]
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// const Nd4jLong time = x->sizeAt(0);
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// const Nd4jLong bS = x->sizeAt(1);
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// const Nd4jLong iS = x->sizeAt(2);
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// const Nd4jLong nUn = hi->sizeAt(1);
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// NDArray<T> h(hi->ordering(), {time, bS, nUn}); // feed forward output
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// // first step, time = 0, feed forward
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// NDArray<T> x0 = (*x)({{0,1}, {}, {}});
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// NDArray<T> h0 = h({{0,1}, {}, {}});
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// helpers::gruCell<T>({&x0, hi, Wx, Wh, b}, &h0);
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// // first step, time = 0, back prop
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// NDArray<T> dLdx0 = (*dLdx)({{0,1}, {}, {}});
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// NDArray<T> dLdh0 = (*dLdh)({{0,1}, {}, {}});
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// helpers::gruCellBP<T>({&x0, hi, Wx, Wh, b, &dLdh0, nullptr, nullptr, nullptr}, {&dLdx0, dLdhi, dLdWx, dLdWh, dLdb});
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// // loop through the rest time steps
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// for (Nd4jLong t = time-1; t > 0; --t) {
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// for (Nd4jLong t = 1; t < time; ++t) {
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// NDArray<T> xt = (*x)({{t,t+1}, {}, {}});
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// NDArray<T> ht = h({{t,t+1}, {}, {}});
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// NDArray<T> ht_1 = h({{t-1,t}, {}, {}});
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// NDArray<T> dLdxt = (*dLdx)({{t,t+1}, {}, {}});
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// NDArray<T> dLdht = (*dLdh)({{t,t+1}, {}, {}});
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// NDArray<T> dLdWxt_1 = dLdWx;
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// NDArray<T> dLdWht_1 = dLdWh;
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// NDArray<T> dLdbt_1 = dLdb;
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// // feed forward, calculation of ht
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// helpers::gruCell<T>({&xt, &ht_1, Wx, Wh, b}, &ht);
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// // back prop
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// helpers::gruCellBP<T>({&xt, &ht_1, Wx, Wh, b, &dLdht, &dLdWxt_1, &dLdWht_1, &dLdbt_1}, {&dLdxt, nullptr, dLdWx, dLdWh, dLdb});
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// }
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// }
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}
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}
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}
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