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cavis/libnd4j/include/ops/declarable/helpers/cuda/gru.cu

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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.
*
* 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
******************************************************************************/
//
// @author Yurii Shyrma (iuriish@yahoo.com), created on 15.02.2018
//
// implementation of gated Recurrent Unit cell
// (cf. http://arxiv.org/abs/1406.1078).
// Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
// "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation"
#include<ops/declarable/helpers/gru.h>
#include <ops/declarable/CustomOperations.h>
#include<ops/declarable/helpers/transforms.h>
#include <MmulHelper.h>
namespace nd4j {
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////////
void gruCell(nd4j::LaunchContext * context, const NDArray* x, const NDArray* hLast, const NDArray* Wru, const NDArray* Wc,
const NDArray* bru, const NDArray* bc,
NDArray* r, NDArray* u, NDArray* c, NDArray* h) {
//Inputs:
// x input [bS x inSize]
// hLast previous cell output [bS x numUnits], that is at previous time step t-1
// Wru RU weights - [bS, 2*numUnits] - reset and update gates
// Wc C weights - [bS, numUnits] - cell gate
// bru r and u biases, [2*numUnits] - reset and update gates
// bc c biases, [numUnits] - cell gate
//Outputs:
// r Reset gate output [bS, numUnits]
// u Update gate output [bS, numUnits]
// c Cell gate output [bS, numUnits]
// h current cell output [bS, numUnits]
const int nIn = x->sizeAt(1);
const int nU = hLast->sizeAt(1); // number of units
//Concat inputs: [x, yt-1]: concat([bs,nIn],[bs,nOut]) -> [bs, (nIn+nOut)]
nd4j::ops::concat concatOp;
std::vector<NDArray*> inputs;
std::vector<double> targs;
std::vector<Nd4jLong> iargs({1}); //Axis = 1
std::vector<bool> bargs;
inputs.emplace_back(const_cast<NDArray*>(x));
inputs.emplace_back(const_cast<NDArray*>(hLast));
auto result = concatOp.execute(inputs, targs, iargs, bargs);
auto concatOut = result->at(0);
//mmul/z for reset and update gates: (x * weight_ux + hLast * weight_xr + b_u)
auto m = mmul(*concatOut, *Wru); //mmul: [bs, (nIn+numUnits)]* [(inSize+numUnits), 2*numUnits] = [bs, 4*numUnits]
m += (*bru);
sigmoidInplace(m); //sigmoid(rz) and sigmoid(uz)
auto mr = m({0,0, 0, nU});
auto mu = m({0,0, nU, 2*nU});
r->assign(&mr);
u->assign(&mu);
//Concatenated inputs: [x, yt-1 .* r]
auto yr = (*concatOut)({0,0, nIn, nIn+nU});
yr *= (*r);
//c = tanh(x * weight_cx + (hLast .* r) * weight_cr + b_c)
MmulHelper::mmul(concatOut, const_cast<NDArray*>(Wc), c, 1.0, 0.0); //c = 1.0 * concatOut * Wc + 0.0 * c
*c += *bc;
tanhInplace(*c);
//Output: h = (1-u).*c + u .* hPrev
//auto hResult = (*u) * (*hLast) + (1.0f - *u) * (*c); const_cast<NDArray*>(h)->assign(&hResult);
u->applyPairwiseTransform(pairwise::Multiply, hLast, h, nullptr); //h = u * hLast
auto temp = (1.0f - *u);
temp *= (*c);
(*h) += temp;
delete result;
}
//////////////////////////////////////////////////////////////////////////
void gruTimeLoop(nd4j::LaunchContext * context, const NDArray* x, const NDArray* h0, const NDArray* Wx, const NDArray* Wh, const NDArray* b, NDArray* h) {
}
//////////////////////////////////////////////////////////////////////////
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,
const NDArray* dLdWh0, const NDArray* dLdb0, NDArray* dLdx, NDArray* dLdh0, NDArray* dLdWx, NDArray* dLdWh, NDArray* dLdb) {
// x input [bS, iS]
// h0 previous cell output [bS, nU], that is at previous time step t-1
// Wx input-to-hidden weights, [iS, 3*nU]
// Wh hidden-to-hidden weights, [nU, 3*nU]
// b biases, [3*nU]
// dLdh gradient wrt output, [bS,nU], that is epsilon_next
// dLdWx0 gradient wrt Wx at previous time step, [iS, 3*nU]
// dLdWh0 gradient wrt Wh at previous time step, [nU, 3*nU]
// dLdb0 gradient wrt b at previous time step, [3*nU]
// dLdx gradient wrt x, [bS, iS], that is epsilon
// dLdh0 gradient wrt h0, [bS, nU]
// dLdWx gradient wrt Wx, [iS, 3*nU]
// dLdWh gradient wrt Wh, [nU, 3*nU]
// dLdb gradient wrt b at previous time step, [3*nU]
// h is current cell output [bS, nU], that is at current time step t
const int nU = h0->sizeAt(1);
// ***** feed forward step ***** //
// gates = sigmoid(x*Wx + h0*Wh + b)
auto gates = sigmoid(mmul(*x, (*Wx)({0,0, 0,2*nU})) + mmul(*h0, (*Wh)({0,0, 0,2*nU})) + (*b)({0,2*nU})); // [bS, 2*nU] + [bS, 2*nU] + [1, 2*nU] = [bS, 2*nU]
// reset gate
auto r = gates({0,0, 0, nU}); // [bS, nU]
// update gate
auto u = gates({0,0, nU, 2*nU}); // [bS, nU]
// ◦ means element-wise product or so called Hadamard product
// n = tanh(x*Wx + (r◦h0)*Wh + b)
auto n = tanh(mmul(*x, (*Wx)({0,0, 2*nU,3*nU})) + mmul((*h0)*r, (*Wh)({0,0, 2*nU,3*nU})) + (*b)({2*nU,3*nU})); // [bS, nU]
// ***** back prop step ***** //
auto Wxr = (*Wx)({0,0, 0, nU});
auto Wxu = (*Wx)({0,0, nU, 2*nU});
auto Wxn = (*Wx)({0,0, 2*nU,3*nU});
auto Whr = (*Wh)({0,0, 0, nU});
auto Whu = (*Wh)({0,0, nU, 2*nU});
auto Whn = (*Wh)({0,0, 2*nU,3*nU});
auto WxrT = Wxr.transpose();
auto WxuT = Wxu.transpose();
auto WxnT = Wxn.transpose();
auto WhrT = Whr.transpose();
auto WhuT = Whu.transpose();
auto WhnT = Whn.transpose();
auto xT = x->transpose();
auto h0T = h0->transpose();
auto dLdWxr = (*dLdWx)({0,0, 0, nU});
auto dLdWxu = (*dLdWx)({0,0, nU, 2*nU});
auto dLdWxn = (*dLdWx)({0,0, 2*nU,3*nU});
auto dLdWhr = (*dLdWh)({0,0, 0, nU});
auto dLdWhu = (*dLdWh)({0,0, nU, 2*nU});
auto dLdWhn = (*dLdWh)({0,0, 2*nU,3*nU});
auto dLdbr = (*dLdb)({0, nU});
auto dLdbu = (*dLdb)({nU, 2*nU});
auto dLdbn = (*dLdb)({2*nU,3*nU});
auto dhdu = *h0 - n; // [bS, nU]
auto dhdn = 1.f - u; // [bS, nU]
auto dSigdu = u * (1.f - u); // [bS, nU]
auto dSigdr = r * (1.f - r); // [bS, nU]
auto dActdn = 1.f - n * n; // [bS, nU]
auto dndr = mmul(dActdn * (*h0), WhnT);
auto drdh0 = mmul(dSigdr, WhrT);
auto dLdn = (*dLdh) * dhdn;
auto dLdu = (*dLdh) * dhdu;
auto dLdr = dLdn * dndr;
dLdx->assign( mmul(dLdu * dSigdu, WxuT) + mmul(dLdr * dSigdr, WxrT) + mmul(dLdn * dActdn, WxnT) ); // [bS,iS]
dLdh0->assign( mmul(dLdu * dSigdu, WhuT) + mmul(dLdn * dActdn * (r + drdh0), WhnT) + (*dLdh)*u ); // [bS,nU]
dLdWxr.assign( mmul(xT, dSigdr * dLdr) ); // [iS,nU]
dLdWhr.assign( mmul(h0T, dSigdr * dLdr) ); // [nU,nU]
dLdWxu.assign( mmul(xT, dSigdu * dLdu) ); // [iS,nU]
dLdWhu.assign( mmul(h0T, dSigdu * dLdu) ); // [nU,nU]
dLdWxn.assign( mmul(xT, dActdn * dLdn) ); // [iS,nU]
dLdWhn.assign( mmul((r*(*h0)).transpose(), dActdn * dLdn) ); // [nU,nU]
dLdbr.assign( (dSigdr * dLdr).reduceAlongDims(reduce::Sum, {0})); // [nU]
dLdbu.assign( (dSigdu * dLdu).reduceAlongDims(reduce::Sum, {0})); // [nU]
dLdbn.assign( (dActdn * dLdn).reduceAlongDims(reduce::Sum, {0})); // [nU]
if(dLdWx0 != nullptr)
*dLdWx += *dLdWx0;
if(dLdWh0 != nullptr)
*dLdWh += *dLdWh0;
if(dLdb0 != nullptr)
*dLdb += *dLdb0;
}
}
}
}
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