chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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/*******************************************************************************
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* Copyright (c) 2021 Deeplearning4j Contributors
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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 AbdelRauf
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//
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#include <execution/ThreadPool.h>
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#include <execution/Threads.h>
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#include <helpers/LoopsCoordsHelper.h>
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#include <ops/declarable/helpers/ctc.h>
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#include <cmath>
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#include <memory>
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#include <stdexcept>
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#include <type_traits>
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#include <system/selective_rendering.h>
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#if NOT_EXCLUDED(OP_ctc_loss)
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namespace sd {
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namespace ops {
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namespace helpers {
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template <bool IsLogPStrided = false, bool IsLblStrided = false, typename Type, typename IndexType>
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Type forward(Type *alphaPtr, const sd::LongType &incA, const Type *logP, const sd::LongType &incP, const IndexType *lbl,
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const sd::LongType &lenSB, const sd::LongType &lenT, const int &blankIndex, int elwiseP = 1,
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int elwiseS = 1) {
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Type negInf = negative_infinity<Type>();
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// initialize alphas at t=0
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alphaPtr[0] = element<IsLogPStrided>(logP, blankIndex, elwiseP);
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// alphaPtr[1] =logP[lbl[0]];
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alphaPtr[1] = element<IsLogPStrided>(logP, *lbl, elwiseP);
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// the rest initialization was skipped
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// as its assumed the array already were initialized with negative infinity
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// move to the next frame
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Type *alphaPrevPtr = alphaPtr;
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alphaPtr += incA;
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logP += incP;
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auto startX = lenSB - 2 * lenT;
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// process the rest
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for (auto t = 1; t < lenT; t++) {
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// start = max(0,L-2*(T-t))
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auto s = startX + 2 * t;
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s = s > 0 ? s : 0;
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for (; s < lenSB; s++) {
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auto ind = s / 2; // our real index
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// we force blanks for even indexes
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// strided version of lbl[ind] => element<IsLblStrided>(lbl, ind, elwiseS)
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auto currentInd = (s % 2 == 0) ? blankIndex : element<IsLblStrided>(lbl, ind, elwiseS);
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// {t-1,s}
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Type alphaS = alphaPrevPtr[s];
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Type alphaS_1 = s > 0 ? alphaPrevPtr[s - 1] : negInf;
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// logP[currentInd] or logP[currentInd*elwiseP]
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auto currentProb = element<IsLogPStrided>(logP, currentInd, elwiseP);
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// if blank or the same as previous
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if (s > 1 && currentInd != blankIndex && currentInd != element<IsLblStrided>(lbl, ind - 1, elwiseS)) {
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Type alphaS_2 = alphaPrevPtr[s - 2];
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alphaPtr[s] = log_sum_exp(alphaS, alphaS_1, alphaS_2) + currentProb;
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} else {
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alphaPtr[s] = log_sum_exp(alphaS, alphaS_1) + currentProb;
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}
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}
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// store t-1 alpha Ptr
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alphaPrevPtr = alphaPtr;
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logP += incP;
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alphaPtr += incA;
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}
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auto logP0 = alphaPrevPtr[lenSB - 1];
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auto logP1 = alphaPrevPtr[lenSB - 2];
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return -log_sum_exp(logP0, logP1);
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}
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//#undef CALCULATE_ALL_IN_ONE_FRAME_LOOP
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template <bool IsLogPStrided = false, bool IsLblStrided = false, bool isGradStrided = false, typename Type,
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typename IndexType = int>
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void backwardAndGrad(Type forwardLogLoss, Type *alphaPtr, Type *bettaPtr, int incA, const Type *logP, int incP,
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Type *gradPtr, int incG, const IndexType *lbl, const sd::LongType &lenS, const sd::LongType &lenT,
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const sd::LongType &lenK, const int &blankIndex, int elwiseP = 1, int elwiseS = 1,
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int elwiseG = 1) {
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Type negInf = negative_infinity<Type>();
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sd::LongType lenSB = 2 * lenS + 1;
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auto origBetta = bettaPtr;
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auto origLogP = logP;
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// move to the last frame
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bettaPtr += (lenT - 1) * incA;
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logP += (lenT - 1) * incP;
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// initialize bettas at t=lenT
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bettaPtr[lenSB - 1] = element<IsLogPStrided>(logP, blankIndex, elwiseP);
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auto lblIndex = element<IsLblStrided>(lbl, lenS - 1, elwiseS);
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bettaPtr[lenSB - 2] = element<IsLogPStrided>(logP, lblIndex, elwiseP); // logP[lbl[lenS - 1]];
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#if defined(CALCULATE_ALL_IN_ONE_FRAME_LOOP)
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// move to the last
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gradPtr += (lenT - 1) * incG;
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alphaPtr += (lenT - 1) * incA;
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for (auto s = lenSB - 1; s >= 0; s--) {
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auto ind = s / 2; // our real index
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// we forced blanks for even indexes
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auto currentInd = (s % 2 == 0) ? blankIndex : element<IsLblStrided>(lbl, ind, elwiseS);
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// alpha(s)*betta(s) in log scale but still store in alpha to save memory
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auto alphaBettaS = alphaPtr[s] + bettaPtr[s];
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// sum (alpha(s)*betta(s) ) over real indexes
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auto ¤tGrad = element<isGradStrided>(gradPtr, currentInd, elwiseG); // gradPtr[currentInd];
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if (currentGrad == negInf) {
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currentGrad = alphaBettaS;
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} else {
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Type cMax = std::max(currentGrad, alphaBettaS);
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currentGrad = std::log(std::exp(currentGrad - cMax) + std::exp(alphaBettaS - cMax)) + cMax;
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}
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}
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for (int k = 0; k < lenK; k++) {
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// compute the rest grad
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// prob(t,k) - grad(k) / ((prob(t,k)*Z) )
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// p2= grad(k) / (prob(t,k)*Z )
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// in logscale . plus we have Z as -logLoss
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// auto p2 = std::exp(gradPtr[k] + forwardLogLoss - logP[k]);
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// gradPtr[k] = std::exp(logP[k]) - p2;
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auto currentProb = element<IsLogPStrided>(logP, k, elwiseP);
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auto ¤tGrad = element<isGradStrided>(gradPtr, k, elwiseG);
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auto p2 = std::exp(currentGrad + forwardLogLoss - currentProb);
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currentGrad = std::exp(currentProb) - p2;
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}
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gradPtr -= incG;
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alphaPtr -= incA;
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#endif
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auto bettaPrevPtr = bettaPtr;
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bettaPtr -= incA;
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logP -= incP;
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// process the rest
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for (auto t = lenT - 2; t >= 0; t--) {
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#if defined(CALCULATE_ALL_IN_ONE_FRAME_LOOP)
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auto end = lenSB - 1;
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#else
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auto end = std::min(2 * t + 2, lenSB - 1);
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#endif
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for (auto s = end; s >= 0; s--) {
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auto ind = s / 2; // our real index
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// we forced blanks for even indexes
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auto currentInd = (s % 2 == 0) ? blankIndex : element<IsLblStrided>(lbl, ind, elwiseS); // lbl[ind];
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// {t-1,s}
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Type bettaS = bettaPrevPtr[s];
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Type bettaS_1 = s < lenSB - 1 ? bettaPrevPtr[s + 1] : negInf;
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// logP[currentInd]
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auto currentProb = element<IsLogPStrided>(logP, currentInd, elwiseP);
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// if blank or the same as previous
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if (s < lenSB - 2 && currentInd != blankIndex && currentInd != element<IsLblStrided>(lbl, ind + 1, elwiseS)) {
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Type bettaS_2 = bettaPrevPtr[s + 2];
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bettaPtr[s] = log_sum_exp(bettaS, bettaS_1, bettaS_2) + currentProb;
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} else {
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bettaPtr[s] = log_sum_exp(bettaS, bettaS_1) + currentProb;
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}
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#if defined(CALCULATE_ALL_IN_ONE_FRAME_LOOP)
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// alpha(s)*betta(s) in log scale but still store in alpha to save memory
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auto alphaBettaS = alphaPtr[s] + bettaPtr[s];
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// sum (alpha(s)*betta(s) ) over real indexes
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auto ¤tGrad = element<isGradStrided>(gradPtr, currentInd, elwiseG); // gradPtr[currentInd];
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if (currentGrad == negInf) {
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currentGrad = alphaBettaS;
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} else {
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Type cMax = std::max(currentGrad, alphaBettaS);
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currentGrad = std::log(std::exp(currentGrad - cMax) + std::exp(alphaBettaS - cMax)) + cMax;
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}
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#endif
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}
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#if defined(CALCULATE_ALL_IN_ONE_FRAME_LOOP)
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for (int k = 0; k < lenK; k++) {
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// compute the rest grad
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// prob(t,k) - grad(k) / ((prob(t,k)*Z) )
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// p2= grad(k) / (prob(t,k)*Z )
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// in logscale . plus we have Z as -logLoss
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// auto p2 = std::exp(gradPtr[k] + forwardLogLoss - logP[k]);
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// gradPtr[k] = std::exp(logP[k]) - p2;
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auto currentProb = element<IsLogPStrided>(logP, k, elwiseP);
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auto ¤tGrad = element<isGradStrided>(gradPtr, k, elwiseG);
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auto p2 = std::exp(currentGrad + forwardLogLoss - currentProb);
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currentGrad = std::exp(currentProb) - p2;
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}
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alphaPtr -= incA;
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gradPtr -= incG;
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#endif
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bettaPrevPtr = bettaPtr;
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bettaPtr -= incA;
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logP -= incP;
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}
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#if !defined(CALCULATE_ALL_IN_ONE_FRAME_LOOP)
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// alpha*betta
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bettaPtr = origBetta;
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logP = origLogP;
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for (int t = 0; t < lenT; t++) {
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for (int s = 0; s < lenSB; s++) {
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auto ind = s / 2; // our real index
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// we forced blanks for even indexes
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auto currentInd = (s % 2 == 0) ? blankIndex : element<IsLblStrided>(lbl, ind, elwiseS); // lbl[ind];
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// alpha(s)*betta(s) in log scale but still store in alpha to save memory
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auto alphaBettaS = alphaPtr[s] + bettaPtr[s];
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// sum (alpha(s)*betta(s) ) over real indexes
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auto ¤tGrad = element<isGradStrided>(gradPtr, currentInd, elwiseG); // gradPtr[currentInd];
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if (currentGrad == negInf) {
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currentGrad = alphaBettaS;
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} else {
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currentGrad = log_sum_exp(currentGrad, alphaBettaS);
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}
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// alphaPtr[s] = alphaBettaS;
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}
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PRAGMA_OMP_SIMD
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for (int k = 0; k < lenK; k++) {
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// compute the rest grad
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// prob(t,k) - grad(k) / ((prob(t,k)*Z) )
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// p2= grad(k) / (prob(t,k)*Z )
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// in logscale . plus we have Z as -logLoss
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// auto p2 = std::exp(gradPtr[k] + forwardLogLoss - logP[k]);
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// gradPtr[k] = std::exp(logP[k]) - p2;
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auto currentProb = element<IsLogPStrided>(logP, k, elwiseP);
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auto ¤tGrad = element<isGradStrided>(gradPtr, k, elwiseG);
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auto p2 = std::exp(currentGrad + forwardLogLoss - currentProb);
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currentGrad = std::exp(currentProb) - p2;
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}
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gradPtr += incG;
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bettaPtr += incA;
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alphaPtr += incA;
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logP += incP;
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}
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#endif
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}
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/**
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* Calculates ctc loss and fills gradients
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* @param logP logits matrix(lenT,lenK) pointer (log soft max input of rnn)
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* @param incP stride of logits for the next time frame
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* @param gradPtr gradient for output
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* @param incG stride of the gradient for the next time frame
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* @param lbl target label
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* @param lenT frame length
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* @param lenK class length
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* @param lenS target label length
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* @param blankIndex index of the blank label in logit class
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*/
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template <bool IsLogPStrided = true, bool IsLblStrided = true, bool IsGradStrided = true, typename Type,
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typename IndexType>
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Type unitLossAndGrad(const Type *logP, int incP, Type *gradPtr, int incG, const IndexType *lbl, int lenT, int lenK,
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int lenS, int blankIndex, int elwiseP = 1, int elwiseS = 1, int elwiseG = 1) {
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auto lenSB = 2 * lenS + 1;
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// create temp Array for holding bettaArr [lenT,lenSB]
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// create temp Array for holding alphaArr [lenT,lenSB]
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int bufferC = gradPtr ? 2 : 1;
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NDArray *bufferArr = NDArrayFactory::create<Type>('c', {bufferC, lenT, lenSB});
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auto bufferPtr = bufferArr->bufferAsT<Type>();
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auto incA = bufferArr->stridesOf()[1];
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auto bettaBufferPtr = bufferPtr + bufferArr->stridesOf()[0];
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Type negInf = negative_infinity<Type>();
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if (gradPtr) {
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if (elwiseG == 1) {
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PRAGMA_OMP_SIMD
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for (int i = 0; i < lenK * lenT; i++) {
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gradPtr[i] = negInf;
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}
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} else {
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auto tempPtr = gradPtr;
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for (int i = 0; i < lenT; i++) {
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for (int j = 0; j < lenK; j++) element<false>(tempPtr, j, elwiseG) = negInf;
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tempPtr += incG;
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}
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}
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}
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// set all vals to neginf
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PRAGMA_OMP_SIMD
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for (int i = 0; i < bufferC * lenSB * lenT; i++) {
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bufferPtr[i] = negInf;
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}
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// forward
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Type logLoss =
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forward<IsLogPStrided, IsLblStrided>(bufferPtr, incA, logP, incP, lbl, lenSB, lenT, blankIndex, elwiseP, elwiseS);
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// backward and gradient if gradptr supplied
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if (gradPtr)
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backwardAndGrad<IsLogPStrided, IsLblStrided, IsGradStrided>(logLoss, bufferPtr, bettaBufferPtr, incA, logP, incP,
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gradPtr, incG, lbl, lenS, lenT, lenK, blankIndex,
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elwiseP, elwiseS, elwiseG);
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delete bufferArr;
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return logLoss;
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}
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template <typename Type, typename IndexType>
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void ctc_loss_(NDArray&logits, NDArray&targetLabels, NDArray&logitsLengths,
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NDArray&targetLabelLengths, NDArray &logLosses, NDArray &gradients, int blankIndex) {
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// lenT - input length of T
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// lenS - lenght of sequence
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// lenSB - length with blanks
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auto lenBatch = logits.shapeOf()[0];
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auto maxLenT = logits.shapeOf()[1];
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auto lenK = logits.shapeOf()[2];
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auto maxLenS = targetLabels.shapeOf()[1];
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// get probability buffer and targetLabels buffer
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auto logP = logits.bufferAsT<Type>();
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auto lblPtr = targetLabels.bufferAsT<IndexType>();
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auto lenTPtr = logitsLengths.bufferAsT<IndexType>();
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auto lenSPtr = targetLabelLengths.bufferAsT<IndexType>();
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auto batchLbl = targetLabels.stridesOf()[0];
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auto batchP = logits.stridesOf()[0];
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auto incP = logits.stridesOf()[1];
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auto elwiseSLen = targetLabelLengths.stridesOf()[0];
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auto elwiseT = logitsLengths.stridesOf()[0];
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auto elwiseS = targetLabels.stridesOf()[1];
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auto elwiseP = logits.stridesOf()[2];
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int elwiseLL = 0;
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Type *logLossPtr = nullptr;
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if (!logLosses.isEmpty()) {
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elwiseLL = logLosses.stridesOf()[0];
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logLossPtr = logLosses.bufferAsT<Type>();
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}
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// defaulting blankIndex to the last class if its incorrect or -1
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if (blankIndex > maxLenS || blankIndex < 0) blankIndex = maxLenS - 1;
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auto func = [logP, batchP, incP, elwiseP, lenK, lenTPtr, lenSPtr, logLossPtr, lblPtr, maxLenT, maxLenS, batchLbl,
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blankIndex, elwiseT, elwiseLL, elwiseSLen, elwiseS,
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&gradients](uint64_t thread_id, int64_t start, int64_t stop, int64_t increment) -> void {
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Type *gradPtr = nullptr;
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Type resultLoss;
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int batchG, incG, elwiseG;
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if (!gradients.isEmpty()) {
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batchG = gradients.stridesOf()[0];
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incG = gradients.stridesOf()[1];
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elwiseG = gradients.stridesOf()[2];
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gradPtr = gradients.bufferAsT<Type>() + start * batchG;
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} else {
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elwiseG = 1;
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}
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auto logPtr = logP + start * batchP;
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auto tempLblPtr = lblPtr + start * batchLbl;
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if (elwiseP == 1 && elwiseS == 1 && elwiseG == 1) {
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// choose ews one
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for (int batchIndex = start; batchIndex < stop; batchIndex += increment) {
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auto lenT = lenTPtr[batchIndex * elwiseT];
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auto lenS = lenSPtr[batchIndex * elwiseSLen];
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lenT = lenT > maxLenT ? maxLenT : lenT;
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lenS = lenS > maxLenS ? maxLenS : lenS;
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if (lenS <= 0 || lenT <= 0) {
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resultLoss = negative_infinity<Type>();
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} else {
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if (lenS > lenT) lenS = lenT;
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resultLoss = unitLossAndGrad<false, false, false, Type, IndexType>(logPtr, incP, gradPtr, incG, tempLblPtr,
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lenT, lenK, lenS, blankIndex);
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}
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if (gradPtr) gradPtr += batchG;
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if (logLossPtr) logLossPtr[batchIndex * elwiseLL] = resultLoss;
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logPtr += batchP;
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tempLblPtr += batchLbl;
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}
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} else {
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// slow strided case for all 3
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for (int batchIndex = start; batchIndex < stop; batchIndex += increment) {
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auto lenT = lenTPtr[batchIndex * elwiseT];
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auto lenS = lenSPtr[batchIndex * elwiseSLen];
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lenT = lenT > maxLenT ? maxLenT : lenT;
|
||||
lenS = lenS > maxLenS ? maxLenS : lenS;
|
||||
if (lenS <= 0 || lenT <= 0) {
|
||||
resultLoss = negative_infinity<Type>();
|
||||
} else {
|
||||
if (lenS > lenT) lenS = lenT;
|
||||
resultLoss = unitLossAndGrad<true, true, true, Type, IndexType>(
|
||||
logPtr, incP, gradPtr, incG, tempLblPtr, lenT, lenK, lenS, blankIndex, elwiseP, elwiseS, elwiseG);
|
||||
}
|
||||
if (gradPtr) gradPtr += batchG;
|
||||
if (logLossPtr) logLossPtr[batchIndex * elwiseLL] = resultLoss;
|
||||
logPtr += batchP;
|
||||
tempLblPtr += batchLbl;
|
||||
}
|
||||
}
|
||||
};
|
||||
samediff::Threads::parallel_for(func, 0, lenBatch, 1);
|
||||
}
|
||||
|
||||
void ctcLoss(graph::Context &block, NDArray&logits, NDArray&targetLabels, NDArray&logitsLengths,
|
||||
NDArray&targetLabelLengths, NDArray &logLosses, NDArray &gradients, int blankIndex) {
|
||||
auto logitsDType = logits.dataType();
|
||||
auto targetLabelsDType = targetLabels.dataType();
|
||||
BUILD_DOUBLE_SELECTOR(logits.dataType(), targetLabels.dataType(), ctc_loss_,
|
||||
(logits, targetLabels, logitsLengths, targetLabelLengths, logLosses, gradients, blankIndex),
|
||||
SD_FLOAT_TYPES, SD_INDEXING_TYPES);
|
||||
}
|
||||
|
||||
BUILD_DOUBLE_TEMPLATE( void ctc_loss_,
|
||||
(NDArray&logits, NDArray&targetLabels, NDArray&logitsLengths,
|
||||
NDArray&targetLabelLengths, NDArray &logLosses, NDArray &gradients, int blankIndex),
|
||||
SD_FLOAT_TYPES, SD_INDEXING_TYPES);
|
||||
|
||||
} // namespace helpers
|
||||
} // namespace ops
|
||||
} // namespace sd
|
||||
#endif
|
||||
Reference in New Issue
Block a user