247 lines
12 KiB
C++
247 lines
12 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2019 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, created on 14.02.2018
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//
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// implementation of operation for LSTM cell with peep hole connections:
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// http://www.bioinf.jku.at/publications/older/2604.pdf
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// S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
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// and
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// https://research.google.com/pubs/archive/43905.pdf
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// Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014.
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#include<ops/declarable/helpers/lstm.h>
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#include <graph/VariableSpace.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 <ops/declarable/helpers/legacy_helpers.h>
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#include <array/NDArrayList.h>
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#include <iterator>
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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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void lstmCell(sd::LaunchContext * context, const NDArray* xt, const NDArray* ht_1, const NDArray* ct_1, const NDArray* Wx, const NDArray* Wh, const NDArray* Wc, const NDArray* Wp, const NDArray* b,
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NDArray* ht, NDArray* ct, const std::vector<double>& params) {
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// xt input [bS x nIn]
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// ht_1 previous cell output [bS x numProj], that is at previous time step t-1, in case of projection=false -> numProj=nOut!!!
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// ct_1 previous cell state [bS x nOut], that is at previous time step t-1
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// Wx input-to-hidden weights, [nIn x 4*nOut]
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// Wh hidden-to-hidden weights, [numProj x 4*nOut]
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// Wc diagonal weights for peephole connections [3*nOut]
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// Wp projection weights [nOut x numProj]
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// b biases, [4*nOut]
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// ht current cell output [bS x numProj], that is at current time step t
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// ct current cell state [bS x nOut], that is at current time step t
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const bool peephole = (bool)params[0]; // if true, provide peephole connections
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const bool projection = (bool)params[1]; // if true, then projection is performed, if false then numProj==nOut is mandatory!!!!
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double clippingCellValue = params[2]; // clipping value for ct, if it is not equal to zero, then cell state is clipped
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double clippingProjValue = params[3]; // clipping value for projected ht, if it is not equal to zero, then projected cell output is clipped
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const double forgetBias = params[4];
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const int bS = xt->sizeAt(0);
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const int nIn = xt->sizeAt(1);
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const int numProj = ht_1->sizeAt(1);
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const int nOut = ct_1->sizeAt(1);
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auto z = mmul(*xt, *Wx) + mmul(*ht_1, *Wh) + *b; // [bS x 4*nOut] + [bS x 4*nOut] + [1 x 4*nOut] = [bS x 4*nOut]
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auto zit = z({0,0, 0,nOut}); // z for input gate, = mmul(Wxi,xt) + mmul(Whi,ht_1) + bi = [bS x nOut]
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auto zft = z({0,0, nOut,2*nOut}); // z for forget gate, = mmul(Wxf,xt) + mmul(Whf,ht_1) + bf = [bS x nOut]
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auto zct = z({0,0, 2*nOut,3*nOut}); // z for cell state, = mmul(Wxc,xt) + mmul(Whc,ht_1) + bc = [bS x nOut]
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auto zot = z({0,0, 3*nOut,4*nOut}); // z for output gate, = mmul(Wxo,xt) + mmul(Who,ht_1) + bo = [bS x nOut]
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if(peephole) { // add peephole connections: z + ct_1*Wc
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zit += (*ct_1) * (*Wc)({0, nOut}); // add peephole connections to input gate
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zft += (*ct_1) * (*Wc)({nOut, 2*nOut}); // add peephole connections to forget gate
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}
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// current sell state = ft*ct_1 + it*tanh(mmul(Wxc,xt) + mmul(Whc,ht_1) + bc
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ct->assign( sigmoid(zft + forgetBias) * (*ct_1) + sigmoid(zit) * tanh(zct) );
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// if clipping value is provided then cell state is clipped by this value prior to the cell output activation
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if(clippingCellValue > 0.0)
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ct->applyScalar(scalar::LstmClip, clippingCellValue, *ct);
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if(peephole)
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zot += (*ct) * (*Wc)({{2*nOut, 3*nOut}}); // add peephole connections to output gate zot + ct*Wc
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// current cell output = ot*tanh(ct)
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auto htNoPeepHole = sigmoid(zot) * tanh(*ct); // = [bS x nOut]
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// apply projection
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if(projection) {
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ht->assign( mmul(htNoPeepHole, *Wp) ); // [bS x nOut] * [ nOut x numProj] = [bS x numProj]
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// if clipping projection is provided then projected cell output state is clipped by this value
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if(clippingProjValue != 0.)
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ht->applyScalar(scalar::LstmClip, clippingProjValue, *ht);
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}
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else
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ht->assign(&htNoPeepHole);
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}
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template <typename T>
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static void fusedTanh(NDArray *z, NDArray *i, NDArray *c, const NDArray *cLast, NDArray *f, NDArray *h) {
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//cell state = blockInput .* inputGate + prevCellState .* forgetGate
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/*
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z->applyPairwiseTransform(pairwise::Multiply, i, c, nullptr); //c = z * i
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auto temp = (*f) * (*cLast);
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*c += temp; //c = (i * z) + (zf * (*cLast))
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c->applyTransform(transform::Tanh, h); //h = tanh(c)
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*/
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auto uLen = static_cast<uint>(z->lengthOf());
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auto c_ = c->bufferAsT<T>();
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auto z_ = z->bufferAsT<T>();
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auto i_ = i->bufferAsT<T>();
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auto f_ = f->bufferAsT<T>();
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auto cLast_ = cLast->bufferAsT<T>();
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auto h_ = h->bufferAsT<T>();
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auto func = PRAGMA_THREADS_FOR {
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for (auto e = start; e < stop; e++) {
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c_[e] = z_[e] * i_[e] + (f_[e] * cLast_[e]);
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h_[e] = sd::math::nd4j_tanh<T, T>(c_[e]);
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}
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};
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sd::Threads::parallel_for(func, 0, uLen);
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}
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//////////////////////////////////////////////////////////////////////////
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void lstmBlockCell(const NDArray* xt, const NDArray* cLast, const NDArray* yLast,
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const NDArray* W, const NDArray* Wci, const NDArray* Wcf, const NDArray* Wco, const NDArray* b,
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NDArray* i, NDArray* c, NDArray* f, NDArray* o, NDArray* z, NDArray* h, NDArray* y, const std::vector<double>& params) {
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/* Input arrays:
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* 0: xt - input [bS, nIn] at time t
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* 1: cLast (cs_prev) - previous cell state [bS, nOut], time t-1
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* 2: yLast (h_prev) - previous output [bS, nOut], time t-1
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* 3: W - Weights - concatenated (input-to-hidden, hidden-to-hidden weights) weights, [(nIn+nOut), 4*nOut]
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* 4: Wci - weights - cell peephole (t-1) connections to input modulation gate, [nOut]
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* 5: Wcf - weights - cell peephole (t-1) connections to forget gate, [nOut]
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* 6: Wco - weights - cell peephole (t) connections to output gate, [nOut]
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* 7: b - biases, [4*nOut]
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*
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* Input integer arguments:
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* 0: if not zero, provide peephole connections
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*
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* Input float arguments:
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* 0: the bias added to forget gates in order to reduce the scale of forgetting in the beginning of the training
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* 1: clipping value for cell state, if it is not equal to zero, then cell state is clipped
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*
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* Output arrays:
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* 0: i - Input modulation gate activations [bS, nOut]
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* 1: c (cs) - Cell state (pre tanh) [bs, nOut] (cs)
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* 2: f - Output - forget gate activations [bs, nOut]
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* 3: o - Output - output gate activations [bs, nOut]
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* 4: z (ci) - Output - block input [bs, nOut]
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* 5: h (co) - Cell state, post tanh [bs, nOut]
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* 6: y (h) - Current cell output [bS, nOut], time t
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*/
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const bool peephole = (bool)params[0]; // if true, provide peephole connections
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const double forgetBias = params[1];
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const double clippingCellValue = params[2]; // clipping value for ct, if it is not equal to zero, then cell state is clipped
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const int bS = xt->sizeAt(0);
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const int nIn = xt->sizeAt(1);
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const int nOut = cLast->sizeAt(1);
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//Concat inputs: [xt, yt-1]: concat([bs,nIn],[bs,nOut]) -> [bs, (nIn+nOut)]
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NDArray concatOut(xt->ordering(), {xt->sizeAt(0), xt->sizeAt(1) + yLast->sizeAt(1)}, xt->dataType(), xt->getContext());
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helpers::concat(xt->getContext(), {const_cast<NDArray*>(xt), const_cast<NDArray*>(yLast)}, concatOut, {1});
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auto m = mmul(concatOut, *W); // mmul: [bs, (nIn+nOut)] * [(nIn+nOut), 4*nOut] = [bs, 4*nOut]
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m += (*b); // addiRowVector
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//Note: weights are ordered [inputGate, blockInput, forgetGate, outputGate] to match TF (TF code comments state [i,f,z/ci,o] but behaviour is [i,z,f,o])
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auto zi = m({0,0, 0, nOut}); // z for input modulation gate, [bS, nOut]
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auto zz = m({0,0, nOut, 2*nOut}); // z for block input, [bS, nOut]
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auto zf = m({0,0, 2*nOut, 3*nOut}); // z for forget gate, [bS, nOut]
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auto zo = m({0,0, 3*nOut, 4*nOut}); // z for output gate, [bS, nOut]
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if(peephole) { // add peephole connections: z + ct_1*Wc
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zi += (*cLast) * (*Wci); // add peephole connections to input gate
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zf += (*cLast) * (*Wcf); // add peephole connections to forget gate
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}
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// current sell state = ft*cLast + it*tanh(mmul(Wxc,xt) + mmul(Whc,ht_1) + bc
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if(forgetBias != 0.0)
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zf += forgetBias;
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PRAGMA_OMP_PARALLEL
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PRAGMA_OMP_SINGLE
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{
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PRAGMA_OMP_TASK
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zz.applyTransform(transform::Tanh, *z); //z = tanh(zz)
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PRAGMA_OMP_TASK
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zi.applyTransform(transform::Sigmoid, *i); //i = sigmoid(zi)
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PRAGMA_OMP_TASK
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zf.applyTransform(transform::Sigmoid, *f); //f = sigmoid(zf);
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}
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if (z->ews() == 1 && i->ews() == 1 && c->ews() == 1 && cLast->ews() == 1 && f->ews() == 1 && h->ews() == 1 &&
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z->ordering() == i->ordering() && z->ordering() == c->ordering() && z->ordering() == cLast->ordering() && z->ordering() == f->ordering() && z->ordering() == h->ordering()) {
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//cell state = blockInput .* inputGate + prevCellState .* forgetGate
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BUILD_SINGLE_SELECTOR(z->dataType(), fusedTanh, (z, i, c, cLast, f, h), FLOAT_TYPES);
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} else {
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//cell state = blockInput .* inputGate + prevCellState .* forgetGate
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z->applyPairwiseTransform(pairwise::Multiply, *i, *c); //c = z * i
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auto temp = (*f) * (*cLast);
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*c += temp; //c = (i * z) + (zf * (*cLast))
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c->applyTransform(transform::Tanh, *h); //h = tanh(c)
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}
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// if clipping value is provided then cell state is clipped by this value prior to the cell output activation
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if(clippingCellValue > 0.0)
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c->applyScalar(scalar::LstmClip, clippingCellValue, *c);
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// add peephole connections to output gate zot + ct*Wc
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if(peephole) {
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auto prod = *c * (*Wco);
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zo += prod;
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}
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zo.applyTransform(transform::Sigmoid, *o); // o = sigmoid(zo)
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// current cell output = ot*tanh(ct)
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c->applyTransform(transform::Tanh, *h); //h = tanh(c)
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o->applyPairwiseTransform(pairwise::Multiply, *h, *y); //y = o * h
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}
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}
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}
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}
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