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NiuTrans.Tensor
Commit 00d7b386 by liyinqiao
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Update the Transformer sample based on the NiuTrans.NMT.

parent 3b93be69
正在显示 包含 849 行增加 和 813 行删除
data/transformer/test/bpevocab 0 → 100644
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data/transformer/test/fbis.test deleted 100644 → 0
Munich 18@@ 56 : Four maps that will change your view of the city
A mental asylum , where today young people are said to meet .
A cryp@@ t chap@@ el , where they are now dig@@ ging t@@ unn@@ els for the S @@@ -@@ @ Bahn .
Al@@ lo@@ t@@ ment holders cul@@ tiv@@ ate the soil of former farmers .
The oldest official map of Munich brings cap@@ tiv@@ ating stories to light .
data/transformer/test/test.de 0 → 100644
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data/transformer/test/test.en 0 → 100644
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data/transformer/train/bpevocab 0 → 100644
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data/transformer/train/fbis.train deleted 100644 → 0
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source/Main.cpp
......@@ -26,7 +26,7 @@
#include "./tensor/core/CHeader.h"
#include "./tensor/test/Test.h"
#include "./sample/fnnlm/FNNLM.h"
#include "./sample/transformer/Transformer.h"
#include "./sample/transformer/NMT.h"
//#define CRTDBG_MAP_ALLOC
//#include <stdlib.h>
......@@ -34,7 +34,7 @@
using namespace nts;
using namespace fnnlm;
using namespace transformer;
using namespace nmt;
int main( int argc, const char ** argv )
{
......@@ -43,7 +43,7 @@ int main( int argc, const char ** argv )
else if(argc > 1 && !strcmp(argv[1], "-fnnlm"))
FNNLMMain(argc - 1, argv + 1);
else if(argc > 1 && !strcmp(argv[1], "-t2t"))
TransformerMain(argc - 1, argv + 1);
NMTMain(argc - 1, argv + 1);
else{
fprintf(stderr, "Thanks for using NiuTensor! This is a library for building\n");
fprintf(stderr, "neural networks in an easy way. \n\n");
......
source/sample/transformer/T2TDecoder.cpp → source/sample/transformer/Decoder.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,15 +19,13 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#include <cmath>
#include "T2TDecoder.h"
#include "module/T2TUtility.h"
#include "module/T2TLayerNormal.h"
#include "module/T2TCommonModules.h"
#include "Decoder.h"
#include "Utility.h"
#include "module/LayerNorm.h"
#include "module/CommonModules.h"
#include "../../tensor/core/CHeader.h"
namespace transformer
namespace nmt
{
/* constructor */
......@@ -64,7 +61,7 @@ AttDecoder::~AttDecoder()
initialize the model
>> config - configurations of the model
*/
void AttDecoder::InitModel(T2TConfig& config)
void AttDecoder::InitModel(Config& config)
{
devID = config.devID;
nlayer = config.nDecLayer;
......@@ -80,16 +77,17 @@ void AttDecoder::InitModel(T2TConfig& config)
/* embedding model */
embedder.InitModel(config, false);
selfAtt = new T2TAttention[nlayer];
fnns = new T2TFNN[nlayer];
selfAttLayerNorms = new T2TLN[nlayer];
enDeAtt = new T2TAttention[nlayer];
enDeAttLayerNorms = new T2TLN[nlayer];
fnnLayerNorms = new T2TLN[nlayer];
selfAtt = new Attention[nlayer];
fnns = new FNN[nlayer];
selfAttLayerNorms = new LN[nlayer];
enDeAtt = new Attention[nlayer];
enDeAttLayerNorms = new LN[nlayer];
fnnLayerNorms = new LN[nlayer];
selfAttCache = new Cache[nlayer];
enDeAttCache = new Cache[nlayer];
if (preNorm)
decoderLayerNorm = new T2TLN;
decoderLayerNorm = new LN;
/* initialize the stacked layers */
for (int i = 0; i < nlayer; i++) {
......@@ -99,6 +97,8 @@ void AttDecoder::InitModel(T2TConfig& config)
fnnLayerNorms[i].InitModel(config);
enDeAtt[i].InitModel(config);
enDeAttLayerNorms[i].InitModel(config);
selfAttCache[i].enable = true;
enDeAttCache[i].enable = true;
}
if (preNorm)
decoderLayerNorm->InitModel(config);
......@@ -115,9 +115,10 @@ make the decoding network
<< return - the output tensor of the decoder
*/
XTensor AttDecoder::Make(XTensor& inputDec, XTensor& outputEnc, XTensor* mask,
XTensor* maskEncDec, int nstep, bool isTraining)
XTensor* maskEncDec, int nstep, bool isTraining)
{
XTensor x;
x = embedder.Make(inputDec, true, isTraining, nstep);
/* dropout */
......@@ -188,8 +189,86 @@ XTensor AttDecoder::Make(XTensor& inputDec, XTensor& outputEnc, XTensor* mask,
}
if (preNorm)
x = decoderLayerNorm->Make(x);
return decoderLayerNorm->Make(x);
return x;
}
/*
make the decoding network
>> inputDec - the input tensor of the decoder
>> outputEnc - the output tensor of the encoder
>> mask - mask that indicates which position is valid
>> maskEncDec - mask for the encoder-decoder attention
>> nstep - the current length of the decoder input
>> isTraining - indicates whether the model is used for training
<< return - the output tensor of the decoder
*/
XTensor AttDecoder::MakeFast(XTensor& inputDec, XTensor& outputEnc, XTensor* mask,
XTensor* maskEncDec, int nstep, bool isTraining)
{
XTensor x;
x = embedder.Make(inputDec, true, isTraining, nstep);
/* dropout */
if (isTraining && dropoutP > 0)
x = Dropout(x, dropoutP);
for (int i = 0; i < nlayer; i++) {
XTensor res;
res = x;
/* layer normalization with pre-norm for self-attn */
x = selfAttLayerNorms[i].Make(x);
/******************/
/* self attention */
x = selfAtt[i].Make(x, x, x, mask, isTraining, &selfAttCache[i], SELF_ATT);
/* dropout */
if (isTraining && dropoutP > 0)
x = Dropout(x, dropoutP);
/* residual connection */
x = Sum(res, x);
res = x;
/* layer normalization with pre-norm for encoder-decoder attention */
x = enDeAttLayerNorms[i].Make(x);
/* encoder-decoder attention */
x = enDeAtt[i].Make(outputEnc, x, outputEnc, maskEncDec,
isTraining, &enDeAttCache[i], EN_DE_ATT);
/* dropout */
if (isTraining && dropoutP > 0)
x = Dropout(x, dropoutP);
/* residual connection */
x = Sum(res, x);
res = x;
/* layer normalization with pre-norm for fnn */
x = fnnLayerNorms[i].Make(x);
/* fnn */
x = fnns[i].Make(x, isTraining);
/* dropout */
if (isTraining && dropoutP > 0)
x = Dropout(x, dropoutP);
/* residual connection */
x = Sum(res, x);
}
x = decoderLayerNorm->Make(x);
return x;
}
}
\ No newline at end of file
source/sample/transformer/T2TDecoder.h → source/sample/transformer/Decoder.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,13 +19,13 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#ifndef __T2TDECODER_H__
#define __T2TDECODER_H__
#ifndef __DECODER_H__
#define __DECODER_H__
#include "T2TEncoder.h"
#include "module/T2TUtility.h"
#include "Encoder.h"
#include "Utility.h"
namespace transformer
namespace nmt
{
class AttDecoder
......@@ -52,28 +51,28 @@ public:
DTYPE dropoutP;
/* embedding of word at each position */
T2TEmbedder embedder;
Embedder embedder;
/* FNN model of each layer */
T2TFNN* fnns;
FNN* fnns;
/* attention model of each layer */
T2TAttention* selfAtt;
Attention* selfAtt;
/* layer normalization for attention */
T2TLN* selfAttLayerNorms;
LN* selfAttLayerNorms;
/* layer normalization for fnn */
T2TLN* fnnLayerNorms;
LN* fnnLayerNorms;
/* layer normalization for decoder */
T2TLN* decoderLayerNorm;
LN* decoderLayerNorm;
/* encoder-decoder attention model of each layer */
T2TAttention* enDeAtt;
Attention* enDeAtt;
/* layer normalization for encoder-decoder attention */
T2TLN* enDeAttLayerNorms;
LN* enDeAttLayerNorms;
/* layer cache list */
Cache* selfAttCache;
......@@ -92,11 +91,15 @@ public:
~AttDecoder();
/* initialize the model */
void InitModel(T2TConfig& config);
void InitModel(Config& config);
/* make the decoding network */
XTensor Make(XTensor& inputDec, XTensor& outputEnc, XTensor* mask,
XTensor* maskEncDec, int nstep, bool isTraining);
/* make the decoding network (pre norm) */
XTensor MakeFast(XTensor& inputDec, XTensor& outputEnc, XTensor* mask,
XTensor* maskEncDec, int nstep, bool isTraining);
};
}
......
source/sample/transformer/T2TEncoder.cpp → source/sample/transformer/Encoder.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,15 +19,13 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#include <cmath>
#include "T2TEncoder.h"
#include "module/T2TUtility.h"
#include "module/T2TLayerNormal.h"
#include "module/T2TCommonModules.h"
#include "Encoder.h"
#include "Utility.h"
#include "module/LayerNorm.h"
#include "module/CommonModules.h"
#include "../../tensor/core/CHeader.h"
namespace transformer
namespace nmt
{
/* constructor */
......@@ -56,7 +53,7 @@ AttEncoder::~AttEncoder()
initialize the model
>> config - configurations for the model
*/
void AttEncoder::InitModel(T2TConfig& config)
void AttEncoder::InitModel(Config& config)
{
devID = config.devID;
......@@ -68,18 +65,18 @@ void AttEncoder::InitModel(T2TConfig& config)
dropoutP = config.dropout;
CheckNTErrors(nlayer >= 1, "We have one encoding layer at least!");
CheckNTErrors(vSize > 1, "set vocabulary size by \"-vsize\"");
CheckNTErrors(vSize > 1, "Set vocabulary size by \"-vsize\"");
/* embedding model */
embedder.InitModel(config);
selfAtt = new T2TAttention[nlayer];
fnns = new T2TFNN[nlayer];
attLayerNorms = new T2TLN[nlayer];
fnnLayerNorms = new T2TLN[nlayer];
selfAtt = new Attention[nlayer];
fnns = new FNN[nlayer];
attLayerNorms = new LN[nlayer];
fnnLayerNorms = new LN[nlayer];
if (preNorm)
encoderLayerNorm = new T2TLN;
encoderLayerNorm = new LN;
/* initialize the stacked layers */
for (int i = 0; i < nlayer; i++) {
......@@ -122,7 +119,7 @@ XTensor AttEncoder::Make(XTensor& input, XTensor* mask, XTensor& maskEncDec, boo
attnBefore = LayerNorm(x, attLayerNorms[i], preNorm, true, false);
/* self attention */
att = selfAtt[i].Make(attnBefore, attnBefore, attnBefore, mask, isTraining, NULL, 0);
att = selfAtt[i].Make(attnBefore, attnBefore, attnBefore, mask, isTraining, NULL, SELF_ATT);
/* dropout */
if (isTraining && dropoutP > 0)
......@@ -151,7 +148,63 @@ XTensor AttEncoder::Make(XTensor& input, XTensor* mask, XTensor& maskEncDec, boo
x = LayerNorm(res, fnnLayerNorms[i], preNorm, false, true);
}
if (preNorm)
x = encoderLayerNorm->Make(x);
return encoderLayerNorm->Make(x);
return x;
}
/*
make the encoding network
>> input - the input tensor of the encoder
>> mask - the mask that indicate each position is valid
>> maskEncDec - no use
>> isTraining - indicates whether the model is used for training
<< return - the output tensor of the encoder
*/
XTensor AttEncoder::MakeFast(XTensor& input, XTensor* mask, XTensor& maskEncDec, bool isTraining)
{
XTensor x;
x = embedder.Make(input, false, isTraining);
/* dropout */
if (isTraining && dropoutP > 0)
x = Dropout(x, dropoutP);
for (int i = 0; i < nlayer; i++) {
XTensor res;
res = x;
/* layer normalization with pre-norm for self-attn */
x = attLayerNorms[i].Make(x);
/* self attention */
x = selfAtt[i].Make(x, x, x, mask, isTraining, NULL, SELF_ATT);
/* dropout */
if (isTraining && dropoutP > 0)
x = Dropout(x, dropoutP);
/* residual connection */
x = Sum(res, x);
res = x;
/* layer normalization with pre-norm for fnn */
x = fnnLayerNorms[i].Make(x);
/* fnn */
x = fnns[i].Make(x, isTraining);
/* dropout */
if (isTraining && dropoutP > 0)
x = Dropout(x, dropoutP);
/* residual connection */
x = Sum(res, x);
}
x = encoderLayerNorm->Make(x);
return x;
}
......
source/sample/transformer/T2TEncoder.h → source/sample/transformer/Encoder.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,25 +19,25 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#ifndef __T2TENCODER_H__
#define __T2TENCODER_H__
#ifndef __ENCODER_H__
#define __ENCODER_H__
#include "module/T2TFNN.h"
#include "module/T2TUtility.h"
#include "module/T2TAttention.h"
#include "module/T2TEmbedding.h"
#include "module/T2TLayerNormal.h"
#include "Utility.h"
#include "module/FNN.h"
#include "module/Attention.h"
#include "module/Embedding.h"
#include "module/LayerNorm.h"
#include "../../network/XNet.h"
using namespace nts;
namespace transformer
namespace nmt
{
/*
base class of the encoder
*/
class T2TEncoder
class Encoder
{
public:
virtual XTensor Make(XTensor& input, XTensor* mask, XTensor& mask2, bool isTraining) = 0;
......@@ -47,7 +46,7 @@ public:
/*
the encoder based on self-attention
*/
class AttEncoder : T2TEncoder
class AttEncoder : Encoder
{
public:
/* device id */
......@@ -73,22 +72,22 @@ public:
int ignored;
/* embedding of word at each position */
T2TEmbedder embedder;
Embedder embedder;
/* FNN model of each layer */
T2TFNN* fnns;
FNN* fnns;
/* attention model of each layer */
T2TAttention* selfAtt;
Attention* selfAtt;
/* layer normalizations for attention */
T2TLN* attLayerNorms;
LN* attLayerNorms;
/* layer normalization for fnn */
T2TLN* fnnLayerNorms;
LN* fnnLayerNorms;
/* layer normalization for encoder */
T2TLN* encoderLayerNorm;
LN* encoderLayerNorm;
/* the location of layer normalization */
bool preNorm;
......@@ -101,11 +100,14 @@ public:
~AttEncoder();
/* initialize the model */
void InitModel(T2TConfig& config);
void InitModel(Config& config);
/* make the encoding network */
XTensor Make(XTensor& input, XTensor* mask, XTensor& maskEncDec, bool isTraining);
/* make the encoding network */
XTensor MakeFast(XTensor& input, XTensor* mask, XTensor& maskEncDec, bool isTraining);
/* make the encoding network (wrapper) */
XTensor Make(XTensor& input, XTensor* mask, bool isTraining);
};
......
source/sample/transformer/T2TModel.h → source/sample/transformer/Model.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,23 +19,22 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#ifndef __T2TMODEL_H__
#define __T2TMODEL_H__
#ifndef __MODEL_H__
#define __MODEL_H__
#include "T2TEncoder.h"
#include "T2TDecoder.h"
#include "module/T2TFNN.h"
#include "module/T2TOutput.h"
#include "module/T2TUtility.h"
#include "module/T2TAttention.h"
#include "Encoder.h"
#include "Decoder.h"
#include "module/FNN.h"
#include "module/Output.h"
#include "Utility.h"
#include "module/Attention.h"
namespace transformer
namespace nmt
{
/* a transformer model that keeps parameters of the encoder,
the decoder and the output layer (softmax). Also, it creates
the network used in transformer. */
class T2TModel
/* a nmt model that keeps parameters of the encoder,
the decoder and the output layer (softmax). */
class Model
{
public:
/* device id */
......@@ -49,7 +47,7 @@ public:
AttDecoder* decoder;
/* output layer */
T2TOutput* outputLayer;
Output* outputLayer;
/* indicates whether the model is running for language modeling */
bool isLM;
......@@ -71,13 +69,16 @@ public:
public:
/* constructor */
T2TModel();
Model();
/* de-constructor */
~T2TModel();
~Model();
/* initialize the model */
void InitModel(T2TConfig& config);
void InitModel(Config& config);
/* print model configurations */
void ShowModelConfig(Config& config);
/* make the encoding network */
XTensor MakeEncoder(XTensor& input, XTensor* mask, bool isTraining);
......
source/sample/transformer/Transformer.cpp → source/sample/transformer/NMT.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2018, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -17,49 +16,47 @@
/*
* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2018-07-31
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-06
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-06, 2020-07
*/
#include <cmath>
#include <ctime>
#include "Transformer.h"
#include "train/T2TTrainer.h"
#include "module/T2TUtility.h"
#include "translate/T2TTranslator.h"
#include "../../tensor/XDevice.h"
#include "../../tensor/XGlobal.h"
#include "../../tensor/XUtility.h"
#include "NMT.h"
#include "train/Trainer.h"
#include "translate/Translator.h"
namespace transformer
namespace nmt
{
int TransformerMain(int argc, const char** argv)
int NMTMain(int argc, const char** argv)
{
if (argc == 0)
return 1;
/* load configurations */
T2TConfig config(argc, argv);
Config config(argc, argv);
srand((unsigned int)time(NULL));
srand(1);
/* train the model */
/* training */
if (strcmp(config.trainFN, "") != 0) {
ENABLE_GRAD;
T2TModel model;
Model model;
model.InitModel(config);
T2TTrainer trainer;
Trainer trainer;
trainer.Init(config);
trainer.Train(config.trainFN, config.validFN, config.modelFN, &model);
}
/* translate the test file */
/* translating */
if (strcmp(config.testFN, "") != 0 && strcmp(config.outputFN, "") != 0) {
/* disable grad flow */
DISABLE_GRAD;
T2TModel model;
Model model;
model.InitModel(config);
T2TTranslator translator;
Translator translator;
translator.Init(config);
translator.Translate(config.testFN, config.srcVocabFN,
config.tgtVocabFN, config.outputFN, &model);
......
source/sample/transformer/Transformer.h → source/sample/transformer/NMT.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -16,29 +15,17 @@
*/
/*
*
* An implementation of the transformer system. See more details
* about FNNLM in
* "Attention Is All You Need" by Vaswani et al.
* https://arxiv.org/pdf/1706.03762.pdf
*
* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2018-07-31
* I start writing the code related to NMT - a long time since my last coding
* work on MT
* An implementation of the NMT system.
*/
#ifndef __TRANSFORMER_H__
#define __TRANSFORMER_H__
#include "../../tensor/XGlobal.h"
#include "../../tensor/XTensor.h"
#include "../../tensor/core/CHeader.h"
#ifndef __NMT_H__
#define __NMT_H__
namespace transformer
namespace nmt
{
/* entrance of the program */
int TransformerMain(int argc, const char** argv);
int NMTMain(int argc, const char** argv);
}
......
source/sample/transformer/module/T2TUtility.cpp → source/sample/transformer/Utility.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -27,13 +26,13 @@
#include <fstream>
#include <sstream>
#include "T2TUtility.h"
#include "../../../tensor/XGlobal.h"
#include "Utility.h"
#include "../../tensor/XGlobal.h"
using namespace nts;
using namespace std;
namespace transformer
namespace nmt
{
/*
......@@ -41,7 +40,7 @@ load configurations from the command
>> argc - number of arguments
>> argv - the list of arguments
*/
T2TConfig::T2TConfig(int argc, const char** argv)
Config::Config(int argc, const char** argv)
{
char** args = new char* [MAX_PARAM_NUM];
for (int i = 0; i < argc; i++) {
......@@ -61,22 +60,26 @@ T2TConfig::T2TConfig(int argc, const char** argv)
ShowParams(argsNum, args);
/* options for the model */
LoadParamInt(argsNum, args, "nhead", &nhead, 8);
LoadParamInt(argsNum, args, "enclayer", &nEncLayer, 1);
LoadParamInt(argsNum, args, "declayer", &nDecLayer, 1);
LoadParamInt(argsNum, args, "nhead", &nhead, 4);
LoadParamInt(argsNum, args, "enclayer", &nEncLayer, 6);
LoadParamInt(argsNum, args, "declayer", &nDecLayer, 6);
LoadParamInt(argsNum, args, "maxrp", &maxRP, 8);
LoadParamInt(argsNum, args, "embsize", &embSize, 256);
LoadParamInt(argsNum, args, "modelsize", &modelSize, 256);
LoadParamInt(argsNum, args, "embsize", &embSize, 512);
LoadParamInt(argsNum, args, "modelsize", &modelSize, 512);
LoadParamInt(argsNum, args, "maxpos", &maxPosLen, 1024);
LoadParamInt(argsNum, args, "fnnhidden", &fnnHiddenSize, modelSize * 4);
LoadParamInt(argsNum, args, "vsize", &srcVocabSize, 10000);
LoadParamInt(argsNum, args, "vsizetgt", &tgtVocabSize, 10000);
LoadParamInt(argsNum, args, "fnnhidden", &fnnHiddenSize, modelSize * 2);
LoadParamInt(argsNum, args, "vsize", &srcVocabSize, 10152);
LoadParamInt(argsNum, args, "vsizetgt", &tgtVocabSize, 10152);
LoadParamInt(argsNum, args, "padid", &padID, 1);
LoadParamInt(argsNum, args, "startid", &startID, 2);
LoadParamInt(argsNum, args, "endid", &endID, 2);
LoadParamBool(argsNum, args, "rpr", &useRPR, false);
LoadParamBool(argsNum, args, "prenorm", &preNorm, false);
LoadParamString(argsNum, args, "model", modelFN, "model.bin");
LoadParamBool(argsNum, args, "prenorm", &preNorm, true);
// TODO: refactor the parameters type to support weight sharing during training
LoadParamInt(argsNum, args, "shareemb", &shareAllEmbeddings, 0);
LoadParamInt(argsNum, args, "sharedec", &shareDecInputOutputWeight, 0);
LoadParamString(argsNum, args, "model", modelFN, "");
LoadParamString(argsNum, args, "srcvocab", srcVocabFN, "vocab.src");
LoadParamString(argsNum, args, "tgtvocab", tgtVocabFN, "vocab.tgt");
......@@ -84,19 +87,20 @@ T2TConfig::T2TConfig(int argc, const char** argv)
LoadParamString(argsNum, args, "train", trainFN, "");
LoadParamString(argsNum, args, "valid", validFN, "");
LoadParamInt(argsNum, args, "dev", &devID, 0);
LoadParamInt(argsNum, args, "wbatch", &wBatchSize, 2048);
LoadParamInt(argsNum, args, "sbatch", &sBatchSize, 1);
LoadParamInt(argsNum, args, "wbatch", &wBatchSize, 4096);
LoadParamInt(argsNum, args, "sbatch", &sBatchSize, 8);
isTraining = (strcmp(trainFN, "") == 0) ? false : true;
LoadParamBool(argsNum, args, "mt", &isMT, true);
LoadParamFloat(argsNum, args, "dropout", &dropout, 0.1);
LoadParamFloat(argsNum, args, "fnndrop", &fnnDropout, 0.0);
LoadParamFloat(argsNum, args, "attdrop", &attDropout, 0.0);
LoadParamFloat(argsNum, args, "dropout", &dropout, 0.3);
LoadParamFloat(argsNum, args, "fnndrop", &fnnDropout, 0.1);
LoadParamFloat(argsNum, args, "attdrop", &attDropout, 0.1);
LoadParamFloat(argc, args, "lrate", &lrate, 1.0F);
LoadParamFloat(argc, args, "lrate", &lrate, 0.0015F);
LoadParamFloat(argc, args, "lrbias", &lrbias, 0);
LoadParamInt(argc, args, "nepoch", &nepoch, 20);
LoadParamInt(argc, args, "nepoch", &nepoch, 50);
LoadParamInt(argc, args, "maxcheckpoint", &maxCheckpoint, 10);
LoadParamInt(argc, args, "nstep", &nstep, 100000);
LoadParamInt(argc, args, "nwarmup", &nwarmup, 3000);
LoadParamInt(argc, args, "nwarmup", &nwarmup, 8000);
LoadParamBool(argc, args, "adam", &useAdam, true);
LoadParamFloat(argc, args, "adambeta1", &adamBeta1, 0.9F);
LoadParamFloat(argc, args, "adambeta2", &adamBeta2, 0.98F);
......@@ -104,9 +108,8 @@ T2TConfig::T2TConfig(int argc, const char** argv)
LoadParamBool(argc, args, "shuffled", &isShuffled, true);
LoadParamFloat(argc, args, "labelsmoothing", &labelSmoothingP, 0.1);
LoadParamInt(argc, args, "nstepcheckpoint", &nStepCheckpoint, -1);
LoadParamBool(argc, args, "epochcheckpoint", &useEpochCheckpoint, false);
LoadParamBool(argc, args, "epochcheckpoint", &useEpochCheckpoint, true);
LoadParamInt(argc, args, "updatestep", &updateStep, 1);
LoadParamBool(argc, args, "debug", &isDebugged, false);
LoadParamBool(argc, args, "sorted", &isLenSorted, false);
LoadParamInt(argc, args, "bufsize", &bufSize, 50000);
......@@ -114,7 +117,7 @@ T2TConfig::T2TConfig(int argc, const char** argv)
LoadParamBool(argc, args, "smallbatch", &isSmallBatch, true);
LoadParamBool(argc, args, "bigbatch", &isBigBatch, false);
LoadParamBool(argc, args, "randbatch", &isRandomBatch, false);
LoadParamInt(argc, args, "bucketsize", &bucketSize, 0);
LoadParamInt(argc, args, "bucketsize", &bucketSize, wBatchSize * 10);
/* options for translating */
LoadParamString(argsNum, args, "test", testFN, "");
......@@ -122,7 +125,7 @@ T2TConfig::T2TConfig(int argc, const char** argv)
LoadParamInt(argsNum, args, "beamsize", &beamSize, 1);
LoadParamBool(argsNum, args, "fp16", &useFP16, false);
LoadParamFloat(argsNum, args, "lenalpha", &lenAlpha, 0.6);
LoadParamFloat(argsNum, args, "maxlenalpha", &maxLenAlpha, 2.0);
LoadParamFloat(argsNum, args, "maxlenalpha", &maxLenAlpha, 1.2);
for (int i = 0; i < argc; i++)
delete[] args[i];
......@@ -136,7 +139,7 @@ load configurations from a file
>> args - the list to store the configurations
format: one option per line, separated by a blank or a tab
*/
int T2TConfig::LoadFromFile(const char* configFN, char** args) {
int Config::LoadFromFile(const char* configFN, char** args) {
ifstream f(configFN, ios::in);
CheckNTErrors(f.is_open(), "unable to open the config file");
......
source/sample/transformer/module/T2TUtility.h → source/sample/transformer/Utility.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2018, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,18 +19,18 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-06
*/
#ifndef __T2TUTILITY_H__
#define __T2TUTILITY_H__
#ifndef __UTILITY_H__
#define __UTILITY_H__
#include <string>
#include <cstdio>
#include "../../../tensor/XList.h"
#include "../../tensor/XList.h"
using namespace std;
using namespace nts;
namespace transformer
namespace nmt
{
#define MAX_PARAM_NUM 100
......@@ -50,8 +49,8 @@ IntList SplitInt(const string& s, const string& delimiter);
FloatList SplitFloat(const string& s, const string& delimiter);
UInt64List SplitToPos(const string& s, const string& delimiter);
/* configurations for t2t */
class T2TConfig {
/* configurations for */
class Config {
public:
/* path to the model */
char modelFN[1024];
......@@ -131,6 +130,12 @@ public:
/* indicates whether the model is running for machine translation */
bool isMT;
/* indicates whether share encoder decoder embeddings */
int shareAllEmbeddings;
/* indicates whether share decoder embeddings and output weights */
int shareDecInputOutputWeight;
/* indicates whether the model is running with FP16 data type */
bool useFP16;
......@@ -164,9 +169,12 @@ public:
/* training epoch number */
int nepoch;
/* traing step number */
/* training step number */
int nstep;
/* the maximum number of saved checkpoints */
int maxCheckpoint;
/* indicates whether we use Adam */
bool useAdam;
......@@ -193,9 +201,6 @@ public:
/* number of batches on which we do model update */
int updateStep;
/* indicates whether we intend to debug the net */
bool isDebugged;
/* indicates whether the sequence is sorted by length */
bool isLenSorted;
......@@ -222,7 +227,7 @@ public:
public:
/* load configurations from the command */
T2TConfig(int argc, const char** argv);
Config(int argc, const char** argv);
/* load configurations from a file */
int LoadFromFile(const char* configFN, char** args);
......
source/sample/transformer/module/T2TAttention.h → source/sample/transformer/module/Attention.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,17 +19,17 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04, 2020-06
*/
#ifndef __T2TATTENTION_H__
#define __T2TATTENTION_H__
#ifndef __ATTENTION_H__
#define __ATTENTION_H__
#include "T2TNNUtil.h"
#include "T2TUtility.h"
#include "NNUtil.h"
#include "../Utility.h"
#include "../../../network/XNet.h"
#include "../../../tensor/core/CHeader.h"
using namespace nts;
namespace transformer
namespace nmt
{
/* attention type */
enum { NONE, SELF_ATT, EN_DE_ATT };
......@@ -50,6 +49,9 @@ public:
/* indicates cache miss if 'true' */
bool miss;
/* indicates whether we use cache */
bool enable;
/* constructor */
Cache();
......@@ -64,7 +66,7 @@ public:
};
/* multi-head attention */
class T2TAttention
class Attention
{
public:
/* device id */
......@@ -74,22 +76,22 @@ public:
int nhead;
/* transformation matrix for Q */
XTensor wq;
XTensor weightQ;
/* bias for Q */
XTensor bq;
XTensor biasQ;
/* transformation matrix for K */
XTensor wk;
XTensor weightK;
/* bias for K */
XTensor bk;
XTensor biasK;
/* transformation matrix for V */
XTensor wv;
XTensor weightV;
/* bias for V */
XTensor bv;
XTensor biasV;
XTensor wBig;
......@@ -99,10 +101,10 @@ public:
XTensor RPEmbK;
/* transformation after dot-product attention */
XTensor wo;
XTensor weightO;
/* bias after dot-product attention */
XTensor bo;
XTensor biasO;
/* size of transformed Q and K */
int dk;
......@@ -124,13 +126,13 @@ public:
public:
/* constructor */
T2TAttention();
Attention();
/* de-constructor */
~T2TAttention();
~Attention();
/* initialize the model */
void InitModel(T2TConfig& config);
void InitModel(Config& config);
/* make the network */
XTensor Make(XTensor& k, XTensor& q, XTensor& v,
......@@ -145,8 +147,10 @@ public:
XTensor MakeRPRAttention(XTensor& k, XTensor& q, XTensor& v,
XTensor* mask, bool isTraining, bool isEnc);
/* generate relative position embeddings */
XTensor GetRPEmbedding(const int lenQ, const int lenKV, const int maxRelativeLen, const bool isEnc);
/* relative position-aware dot-product attention inner calculation */
XTensor RPDotProduct(XTensor& x, XTensor& y, XTensor& z, const bool is_key);
};
}
......
source/sample/transformer/module/T2TCommonModules.cpp → source/sample/transformer/module/CommonModules.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northestern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,13 +19,11 @@
* This file includes some common modules of the Transformer model
*/
#include <cmath>
#include "T2TCommonModules.h"
#include "CommonModules.h"
#include "../../../tensor/core/CHeader.h"
#include "../../../tensor/function/FHeader.h"
namespace transformer
namespace nmt
{
/*
......@@ -37,7 +34,7 @@ flexible layer normalization for the Transformer
>> before - whether we use layernorm before attention/fnn
>> after - whether we use layernorm after attention/fnn
*/
XTensor LayerNorm(XTensor& input, T2TLN& ln, bool prenorm, bool before, bool after)
XTensor LayerNorm(XTensor& input, LN& ln, bool prenorm, bool before, bool after)
{
if (after ^ prenorm)
return ln.Make(input);
......
source/sample/transformer/module/T2TCommonModules.h → source/sample/transformer/module/CommonModules.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northestern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -22,16 +21,16 @@
#ifndef __COMMONMODULE_H__
#define __COMMONMODULE_H__
#include "T2TLayerNormal.h"
#include "T2TCommonModules.h"
#include "LayerNorm.h"
#include "CommonModules.h"
using namespace nts;
namespace transformer
namespace nmt
{
/* the layer normalization module to control pre-norm or post-norm*/
XTensor LayerNorm(XTensor& input, T2TLN& ln, bool prenorm, bool before, bool after);
XTensor LayerNorm(XTensor& input, LN& ln, bool prenorm, bool before, bool after);
}
......
source/sample/transformer/module/T2TEmbedding.cpp → source/sample/transformer/module/Embedding.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,17 +19,15 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-07
*/
#include <cmath>
#include "T2TUtility.h"
#include "T2TEmbedding.h"
#include "Embedding.h"
#include "../Utility.h"
#include "../../../tensor/core/CHeader.h"
namespace transformer
namespace nmt
{
/* constructor */
T2TEmbedder::T2TEmbedder()
Embedder::Embedder()
{
devID = -1;
vSize = -1;
......@@ -38,7 +35,7 @@ T2TEmbedder::T2TEmbedder()
}
/* de-constructor */
T2TEmbedder::~T2TEmbedder()
Embedder::~Embedder()
{
}
......@@ -47,7 +44,7 @@ initialize the model
>> config - configurations of the model
>> isEnc - indicates if it is used for the encoder
*/
void T2TEmbedder::InitModel(T2TConfig& config, bool isEnc)
void Embedder::InitModel(Config& config, bool isEnc)
{
devID = config.devID;
d = config.modelSize;
......@@ -70,7 +67,7 @@ void T2TEmbedder::InitModel(T2TConfig& config, bool isEnc)
make positional embeddings (of size eSize * length)
>> length - length of the sequence
*/
void T2TEmbedder::MakePosEmbedding(int length)
void Embedder::MakePosEmbedding(int length)
{
InitTensor2D(&posEmbeddingBase, length, eSize, X_FLOAT, devID);
......@@ -110,58 +107,45 @@ make the network
>> isTraining - indicates whether it is training
<< return - word & position embeddings of the input
*/
XTensor T2TEmbedder::Make(XTensor& input, bool isDec, bool isTraining, int nstep)
XTensor Embedder::Make(XTensor& input, bool isDec, bool isTraining, int nstep)
{
/* make sure the padding index is 1 */
CheckNTErrors(input.order > 1, "Wrong input tensor size!");
CheckNTErrors(input.dimSize[input.order - 1] < maxLength, "The sequence is too long!");
CheckNTErrors(vSize > 0, "set vocabulary size by \"-vsize\"");
CheckNTErrors(eSize > 0, "set embedding size by \"-esize\"");
CheckNTErrors(vSize > 0, "Set vocabulary size by \"-vsize\"");
CheckNTErrors(eSize > 0, "Set embedding size by \"-esize\"");
XTensor wordEmbedding, position, posEmbedding;
InitTensor(&position, &input);
int* posData = new int[input.unitNum];
XTensor inputCPU;
InitTensorOnCPU(&inputCPU, &input);
_CopyValues(&input, &inputCPU);
InitTensor1D(&position, input.GetDim(-1), X_INT, devID);
if (!isDec)
if (!isDec || isTraining || input.GetDim(-1) > 1)
{
/* encoder embeddings */
for (int i = 0; i < inputCPU.dimSize[0]; i++) {
int startNoPad = 1 + 1;
int* p = ((int*)inputCPU.data) + i * inputCPU.dimSize[1];
for (int j = 0; j < inputCPU.dimSize[1]; j++) {
if (p[j] == 1) {
posData[i * inputCPU.dimSize[1] + j] = 1;
}
else {
posData[i * inputCPU.dimSize[1] + j] = startNoPad++;
}
}
}
position.SetData(posData, position.unitNum);
position.Range(0, position.unitNum, 1);
// disable grad
ScaleAndShiftMe(position, 1.0F, float(padIdx + 1));
}
else
{
/* decoder embeddings */
position.SetDataFixed(nstep + 2);
/* decoder embeddings during decoding */
position.SetDataFixed(nstep + padIdx + 1);
}
delete[] posData;
/* we make positional embeddings first */
posEmbedding = Gather(posEmbeddingBase, position);
XTensor embTMP;
embTMP = Gather(posEmbeddingBase, position);
posEmbedding = Unsqueeze(embTMP, 0, input.GetDim(0));
/* then we make word embeddings */
//w.enableGrad = false;
wordEmbedding = Gather(w, input);
wordEmbedding = Linear(wordEmbedding, (float)sqrt((float)eSize));
/* we sum over the two embeddings */
return wordEmbedding + posEmbedding;
SumMe(wordEmbedding, posEmbedding);
return wordEmbedding;
}
}
\ No newline at end of file
source/sample/transformer/module/T2TEmbedding.h → source/sample/transformer/module/Embedding.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,15 +19,15 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-07
*/
#ifndef __T2TEMBEDDING_H__
#define __T2TEMBEDDING_H__
#ifndef __EMBEDDING_H__
#define __EMBEDDING_H__
#include "T2TUtility.h"
#include "../Utility.h"
#include "../../../network/XNet.h"
using namespace nts;
namespace transformer
namespace nmt
{
#define DEFAULT_EMBEDDING_SIZE 512
......@@ -37,7 +36,7 @@ namespace transformer
embedding (of word at position i):
word embedding + positional embedding
*/
class T2TEmbedder
class Embedder
{
public:
/* device id */
......@@ -52,7 +51,7 @@ public:
/* maximum length of the sequence */
int maxLength;
/* dimension size of the hidden layers in the t2t model */
/* dimension size of the hidden layers in the model */
int d;
/* padding index */
......@@ -67,13 +66,13 @@ public:
public:
/* constructor */
T2TEmbedder();
Embedder();
/* de-constructor */
~T2TEmbedder();
~Embedder();
/* initialize the model */
void InitModel(T2TConfig& config, bool isEnc = true);
void InitModel(Config& config, bool isEnc = true);
/* make positional embeddings */
void MakePosEmbedding(int length);
......
source/sample/transformer/module/T2TFNN.cpp → source/sample/transformer/module/FNN.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,19 +19,17 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#include <cmath>
#include "T2TFNN.h"
#include "T2TUtility.h"
#include "T2TEmbedding.h"
#include "FNN.h"
#include "Embedding.h"
#include "../Utility.h"
#include "../../../tensor/core/CHeader.h"
#include "../../../tensor/function/FHeader.h"
namespace transformer
namespace nmt
{
/* constructor */
T2TFNN::T2TFNN()
FNN::FNN()
{
inSize = -1;
outSize = -1;
......@@ -40,7 +37,7 @@ T2TFNN::T2TFNN()
}
/* de-constructor */
T2TFNN::~T2TFNN()
FNN::~FNN()
{
}
......@@ -50,7 +47,7 @@ initialize the model
>> argv - list of pointers to the arguments
>> config - configurations of the model
*/
void T2TFNN::InitModel(T2TConfig& config)
void FNN::InitModel(Config& config)
{
devID = config.devID;
......@@ -69,6 +66,9 @@ void T2TFNN::InitModel(T2TConfig& config)
_SetDataFanInOut(&w1, scale);
_SetDataFanInOut(&w2, scale);
w1.SetDataRand(-(DTYPE)sqrt(6.0F / inSize), (DTYPE)sqrt(6.0F / inSize));
w2.SetDataRand(-(DTYPE)sqrt(6.0F / hSize), (DTYPE)sqrt(6.0F / hSize));
b1.SetZeroAll();
b2.SetZeroAll();
}
......@@ -79,7 +79,7 @@ y = max(0, x * w1 + b1) * w2 + b2
>> input - the input tensor
>> return - the output tensor
*/
XTensor T2TFNN::Make(XTensor& input, bool isTraining)
XTensor FNN::Make(XTensor& input, bool isTraining)
{
XTensor t1;
......
source/sample/transformer/module/T2TFNN.h → source/sample/transformer/module/FNN.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,20 +19,20 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#ifndef __T2TFNN_H__
#define __T2TFNN_H__
#ifndef __FNN_H__
#define __FNN_H__
#include "T2TUtility.h"
#include "T2TLayerNormal.h"
#include "LayerNorm.h"
#include "../Utility.h"
#include "../../../tensor/XTensor.h"
using namespace nts;
namespace transformer
namespace nmt
{
/* a fnn: y = max(0, x * w1 + b1) * w2 + b2 */
class T2TFNN
class FNN
{
public:
/* device id */
......@@ -66,13 +65,13 @@ public:
public:
/* constructor */
T2TFNN();
FNN();
/* de-constructor */
~T2TFNN();
~FNN();
/* initialize the model */
void InitModel(T2TConfig& config);
void InitModel(Config& config);
/* make the network */
XTensor Make(XTensor& input, bool isTraining);
......
source/sample/transformer/module/T2TGatedLinearUnit.cpp → source/sample/transformer/module/GLU.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -19,16 +18,13 @@
* $Created by: Bei Li (libei_neu@outlook.com) 2020-02-03
*/
#include <cmath>
#include "T2TUtility.h"
#include "T2TEmbedding.h"
#include "T2TGatedLinearUnit.h"
#include "GLU.h"
#include "Embedding.h"
#include "../Utility.h"
#include "../../../tensor/core/CHeader.h"
#include "../../../tensor/function/FHeader.h"
namespace transformer
namespace nmt
{
/* constructor */
......@@ -48,7 +44,7 @@ GLU::~GLU()
initialize the model
>> config - configurations of the model
*/
void GLU::InitModel(T2TConfig& config)
void GLU::InitModel(Config& config)
{
devID = config.devID;
......
source/sample/transformer/module/T2TGatedLinearUnit.h → source/sample/transformer/module/GLU.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -23,12 +22,11 @@
#ifndef __GLU_H__
#define __GLU_H__
#include "T2TLayerNormal.h"
#include "T2TGatedLinearUnit.h"
#include "LayerNorm.h"
using namespace nts;
namespace transformer
namespace nmt
{
/* a fnn: y = max(0, x * w1 + b1) * w2 + b2 */
......@@ -68,7 +66,7 @@ public:
~GLU();
/* initialize the model */
void InitModel(T2TConfig& config);
void InitModel(Config& config);
/* make the network */
XTensor Make(XTensor& input);
......
source/sample/transformer/module/T2TLayerHistory.cpp → source/sample/transformer/module/LayerHistory.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -19,19 +18,16 @@
* $Created by: Bei Li (libei_neu@outlook.com) 2020-02-03
*/
#include <cmath>
#include "T2TUtility.h"
#include "T2TEmbedding.h"
#include "T2TLayerNormal.h"
#include "T2TLayerHistory.h"
#include "Embedding.h"
#include "LayerNorm.h"
#include "LayerHistory.h"
#include "../Utility.h"
#include "../../../tensor/core/CHeader.h"
#define SAFE_DELETE(x) do{ if((x) != NULL){delete (x); (x) = NULL;} } while(false)
#define SAFE_DELETE_ARRAY(x) do{ if((x) != NULL) {delete [] (x); (x)=NULL;} } while(false)
namespace transformer
namespace nmt
{
/* constructor */
......@@ -54,7 +50,7 @@ LayerHistory::~LayerHistory()
initialize the model
>> config - configurations of the model
*/
void LayerHistory::InitModel(T2TConfig& config)
void LayerHistory::InitModel(Config& config)
{
devID = config.devID;
d = config.modelSize;
......@@ -62,7 +58,7 @@ void LayerHistory::InitModel(T2TConfig& config)
InitTensor2D(&weight, nlayer + 1, nlayer + 1, X_FLOAT, devID);
layerNorms = new T2TLN[nlayer];
layerNorms = new LN[nlayer];
/* initialize the layer normalization of each layer */
for (int i = 0; i < nlayer; i++) {
......
source/sample/transformer/module/T2TLayerHistory.h → source/sample/transformer/module/LayerHistory.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -22,14 +21,14 @@
#ifndef __LAYERHISTORY_H__
#define __LAYERHISTORY_H__
#include "T2TLayerNormal.h"
#include "T2TLayerHistory.h"
#include "LayerNorm.h"
#include "LayerHistory.h"
#include "../../../tensor/function/FHeader.h"
using namespace nts;
namespace transformer
namespace nmt
{
/*
......@@ -61,7 +60,7 @@ public:
TensorList history;
/* layer normalization for each intimidate layer */
T2TLN* layerNorms;
LN* layerNorms;
public:
/* constructor */
......@@ -71,7 +70,7 @@ public:
~LayerHistory();
/* initialize the model */
void InitModel(T2TConfig& config);
void InitModel(Config& config);
/* add the layer output to the history */
void Add(XTensor& tensor);
......
source/sample/transformer/module/T2TLayerNormal.cpp → source/sample/transformer/module/LayerNorm.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,24 +19,23 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#include <cmath>
#include "T2TUtility.h"
#include "T2TEmbedding.h"
#include "T2TLayerNormal.h"
#include "Embedding.h"
#include "LayerNorm.h"
#include "../Utility.h"
#include "../../../tensor/core/CHeader.h"
namespace transformer
namespace nmt
{
/* constructor */
T2TLN::T2TLN()
LN::LN()
{
devID = -1;
d = 0;
}
/* de-constructor */
T2TLN::~T2TLN()
LN::~LN()
{
}
......@@ -47,7 +45,7 @@ initialize the model
>> argv - list of pointers to the arguments
>> config - configurations of the model
*/
void T2TLN::InitModel(T2TConfig& config)
void LN::InitModel(Config& config)
{
devID = config.devID;
......@@ -57,6 +55,8 @@ void T2TLN::InitModel(T2TConfig& config)
InitTensor1D(&b, d, X_FLOAT, devID);
w.SetDataRand(1.0F, 1.0F);
b.SetZeroAll();
w.SetDataFixed(1);
}
/*
......@@ -64,7 +64,7 @@ make the network
>> input - the input tensor
>> return - layer normalization output
*/
XTensor T2TLN::Make(XTensor& input)
XTensor LN::Make(XTensor& input)
{
XTensor& x = input;
XTensor xn;
......
source/sample/transformer/module/T2TLayerNormal.h → source/sample/transformer/module/LayerNorm.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,20 +19,20 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#ifndef __T2TLAYERNORMAL_H__
#define __T2TLAYERNORMAL_H__
#ifndef __LAYERNORMAL_H__
#define __LAYERNORMAL_H__
#include "T2TUtility.h"
#include "../../../network/XNet.h"
#include "../Utility.h"
#include "../../../network//XNet.h"
using namespace nts;
namespace transformer
namespace nmt
{
/* layer normalization: y = norm(x) * w + b
where norm(x) = (x - mean)/standardDeviation */
class T2TLN
class LN
{
public:
/* device id */
......@@ -50,13 +49,13 @@ public:
public:
/* constructor */
T2TLN();
LN();
/* de-constructor */
~T2TLN();
~LN();
/* initialize the model */
void InitModel(T2TConfig& config);
void InitModel(Config& config);
/* make the network */
XTensor Make(XTensor& input);
......
source/sample/transformer/module/T2TNNUtil.cpp → source/sample/transformer/module/NNUtil.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -16,12 +15,12 @@
*/
/*
* $Created by: Chi (huchinlp@foxmail.com) 2020-03-21
* $Created by: HU Chi (huchinlp@foxmail.com) 2020-03-21
*/
#include "T2TNNUtil.h"
#include "NNUtil.h"
namespace transformer
namespace nmt
{
/*
......
source/sample/transformer/module/T2TNNUtil.h → source/sample/transformer/module/NNUtil.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -16,11 +15,11 @@
*/
/*
* $Created by: Chi (huchinlp@foxmail.com) 2020-03-21
* $Created by: HU Chi (huchinlp@foxmail.com) 2020-03-21
*/
#ifndef __T2TNNUTIL_H__
#define __T2TNNUTIL_H__
#ifndef __NNUTIL_H__
#define __NNUTIL_H__
#include "../../../tensor/XGlobal.h"
#include "../../../tensor/core/CHeader.h"
......@@ -28,7 +27,7 @@
using namespace nts;
namespace transformer
namespace nmt
{
/* the gather function for tensor with any dimension */
......
source/sample/transformer/module/T2TOutput.cpp → source/sample/transformer/module/Output.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,18 +19,16 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#include <cmath>
#include "T2TOutput.h"
#include "T2TUtility.h"
#include "T2TEmbedding.h"
#include "Output.h"
#include "Embedding.h"
#include "../Utility.h"
#include "../../../tensor/core/CHeader.h"
namespace transformer
namespace nmt
{
/* constructor */
T2TOutput::T2TOutput()
Output::Output()
{
devID = -1;
vSize = -1;
......@@ -39,7 +36,7 @@ T2TOutput::T2TOutput()
}
/* de-constructor */
T2TOutput::~T2TOutput()
Output::~Output()
{
}
......@@ -47,7 +44,7 @@ T2TOutput::~T2TOutput()
initialize the model
>> config - configurations of the model
*/
void T2TOutput::InitModel(T2TConfig& config)
void Output::InitModel(Config& config)
{
devID = config.devID;
hSize = config.modelSize;
......@@ -66,7 +63,7 @@ make the network (redefined output tensor)
>> isTraining - whether it is used for training
>> normalized - whether ignore the log-softmax
*/
void T2TOutput::Make(XTensor& input, XTensor& output, bool isTraining, bool normalized)
void Output::Make(XTensor& input, XTensor& output, bool isTraining, bool normalized)
{
XTensor& x = input;
......
source/sample/transformer/module/T2TOutput.h → source/sample/transformer/module/Output.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,19 +19,19 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#ifndef __T2TOUTPUT_H__
#define __T2TOUTPUT_H__
#ifndef __OUTPUT_H__
#define __OUTPUT_H__
#include "T2TUtility.h"
#include "../Utility.h"
#include "../../../tensor/function/FHeader.h"
using namespace nts;
namespace transformer
namespace nmt
{
/* output layer */
class T2TOutput
class Output
{
public:
/* device id */
......@@ -49,13 +48,13 @@ public:
public:
/* constructor */
T2TOutput();
Output();
/* de-constructor */
~T2TOutput();
~Output();
/* initialize the model */
void InitModel(T2TConfig& config);
void InitModel(Config& config);
/* make the network (redefined output tensor) */
void Make(XTensor& input, XTensor& output, bool isTraining, bool normalized);
......
source/sample/transformer/train/T2TBatchLoader.h deleted 100644 → 0
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://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.
*/
/*
* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2019-04-25
* it is cold today but I'll move to a warm place tomorrow :)
*/
#ifndef __T2TBATCHLOADER_H__
#define __T2TBATCHLOADER_H__
#include "../module/T2TUtility.h"
#include "../../../network/XNet.h"
using namespace nts;
namespace transformer
{
#define MAX_SEQUENCE_LENGTH 1024 * 4
/* node to keep batch information */
struct BatchNode
{
/* beginning position */
int beg;
/* end position */
int end;
/* maximum word number on the encoder side */
int maxEnc;
/* maximum word number on the decoder side */
int maxDec;
/* a key for sorting */
int key;
};
class T2TBatchLoader
{
public:
/* buffer for loading words */
int* buf;
/* another buffer */
int* buf2;
/* batch buf */
BatchNode* bufBatch;
/* buffer size */
int bufSize;
/* size of batch buffer */
int bufBatchSize;
/* length of each sequence */
int* seqLen;
/* another array */
int* seqLen2;
/* offset of the first word for each sequence */
int* seqOffset;
/* number of sequences in the buffer */
int nseqBuf;
/* offset for next sequence in the buffer */
int nextSeq;
/* offset for next batch */
int nextBatch;
/* indicates whether we double the </s> symbol for the output of LM */
bool isDoubledEnd;
/* indicates whether we use batchsize = max * sc
rather rather than batchsize = word-number, where max is the maximum
length and sc is the sentence number */
bool isSmallBatch;
/* counterpart of "isSmallBatch" */
bool isBigBatch;
/* randomize batches */
bool isRandomBatch;
/* bucket size */
int bucketSize;
public:
/* constructor */
T2TBatchLoader();
/* de-constructor */
~T2TBatchLoader();
/* initialization */
void Init(T2TConfig& config);
/* load data to buffer */
int LoadBuf(FILE* file, bool isSorted, int step);
/* clear data buffer */
void ClearBuf();
/* set the random batch flag */
void SetRandomBatch(bool flag = true);
/* load a batch of sequences */
int LoadBatch(FILE* file, bool isLM,
XTensor* batchEnc, XTensor* paddingEnc,
XTensor* batchDec, XTensor* paddingDec,
XTensor* gold, XTensor* label,
int* seqs,
int vsEnc, int vsDec, int sBatch, int wBatch,
bool isSorted, int& ws, int& wCount,
int devID, bool isTraining);
/* load a batch of sequences (for language modeling) */
int LoadBatchLM(FILE* file,
XTensor* batchEnc, XTensor* paddingEnc,
XTensor* batchDec, XTensor* paddingDec,
XTensor* gold, XTensor* label,
int* seqs, int vs, int sBatch, int wBatch,
bool isSorted, int& wCount,
int devID, bool isTraining);
/* load a batch of sequences (for machine translation) */
int LoadBatchMT(FILE* file,
XTensor* batchEnc, XTensor* paddingEnc,
XTensor* batchDec, XTensor* paddingDec,
XTensor* gold, XTensor* label,
int* seqs, int vsEnc, int vsDec, int sBatch, int wBatch,
bool isSorted, int& ws, int& wCount,
int devID, bool isTraining);
/* shuffle the data file */
void Shuffle(const char* srcFile, const char* tgtFile);
};
}
#endif
\ No newline at end of file
source/sample/transformer/train/TrainDataSet.h 0 → 100644
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://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.
*/
/*
* $Created by: HU Chi (huchinlp@foxmail.com) 2019-04-03
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-06
*/
#ifndef __TRAIN_DATASET_H__
#define __TRAIN_DATASET_H__
#include <cstdio>
#include <vector>
#include <fstream>
#include "../../../tensor/XList.h"
#include "../../../tensor/XTensor.h"
#include "../../../tensor/XGlobal.h"
#define MAX_WORD_NUM 120
using namespace std;
namespace nts {
/* a class of sentence pairs for training */
struct TrainExample {
/* id of the sentence pair */
int id;
/* source language setence (tokenized) */
IntList srcSent;
/* target language setence (tokenized) */
IntList tgtSent;
/* the key used to shuffle items in a bucket */
int key;
/* the key used to shuffle buckets */
int bucketKey;
};
/* A `TrainDataSet` is associated with a file which contains training data. */
struct TrainDataSet {
public:
/* the data buffer */
TrainBufferType buffer;
/* a list of empty line number */
IntList emptyLines;
/* the pointer to file stream */
FILE* fp;
/* current index in the buffer */
size_t curIdx;
/* size of used data in the buffer */
size_t bufferUsed;
/* size of the bucket used for grouping sentences */
size_t bucketSize;
/* indicates whether it is used for training */
bool isTraining;
public:
/* sort the input by length (in descending order) */
void SortByLength();
/* sort buckets by key (in descending order) */
void SortBucket();
/* sort the output by key (in descending order) */
void SortInBucket(int begin, int end);
/* load data from a file to the buffer */
void LoadDataToBuffer();
/* generate a mini-batch */
UInt64List LoadBatch(XTensor* batchEnc, XTensor* paddingEnc,
XTensor* batchDec, XTensor* paddingDec, XTensor* label,
size_t minSentBatch, size_t batchSize, int devID);
/* initialization function */
void Init(const char* dataFile, int bucketSize, bool training);
/* check if the buffer is empty */
bool IsEmpty();
/* reset the buffer */
void ClearBuf();
/* group data into buckets with similar length */
void BuildBucket();
/* de-constructor */
~TrainDataSet();
};
}
#endif // __TRAIN_DATASET_H__
\ No newline at end of file
source/sample/transformer/train/T2TTrainer.h → source/sample/transformer/train/Trainer.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -19,25 +18,24 @@
* $Created by: XIAO Tong (xiaotong@mail.neu.edu.cn) 2018-08-02
*/
#ifndef __T2TTRAINER_H__
#define __T2TTRAINER_H__
#ifndef __TRAINER_H__
#define __TRAINER_H__
#include "../T2TModel.h"
#include "T2TBatchLoader.h"
#include "../../../tensor/function/FHeader.h"
#include "../Model.h"
#include "TrainDataSet.h"
using namespace nts;
namespace transformer
namespace nmt
{
/* trainer of the T2T model */
class T2TTrainer
/* trainer of the model */
class Trainer
{
public:
/* configurations */
T2TConfig* cfg;
Config* cfg;
/* dimension size of each inner layer */
int d;
......@@ -63,12 +61,18 @@ public:
/* word batch size */
int wBatchSize;
/* size of bucket for grouping data by length */
int bucketSize;
/* training epoch number */
int nepoch;
/* traing step number */
int nstep;
/* the maximum number of saved checkpoints */
int maxCheckpoint;
/* indicates whether we use adam */
bool useAdam;
......@@ -100,39 +104,36 @@ public:
/* number of batches on which we do model update */
int updateStep;
/* indicates whether we intend to debug the net */
bool isDebugged;
/* indicates whether the sequence is sorted by length */
bool isLenSorted;
/* for batching */
T2TBatchLoader batchLoader;
/* used for loading batches */
TrainDataSet batchLoader;
public:
/* constructor */
T2TTrainer();
Trainer();
/* de-constructor */
~T2TTrainer();
~Trainer();
/* initialize the trainer */
void Init(T2TConfig& config);
void Init(Config& config);
/* train the model */
void Train(const char* fn, const char* validFN, const char* modelFN, T2TModel* model);
void Train(const char* fn, const char* validFN, const char* modelFN, Model* model);
/* test the model */
void Validate(const char* fn, const char* ofn, T2TModel* model);
void Validate(const char* fn, const char* ofn, Model* model);
/* make a checkpoint */
void MakeCheckpoint(T2TModel* model, const char* validFN, const char* modelFN, const char* label, int id);
void MakeCheckpoint(Model* model, const char* validFN, const char* modelFN, const char* label, int id);
/* update the model by delta rule */
void Update(T2TModel* model, const float lr);
void Update(Model* model, const float lr);
/* prepare model for training */
void PrepareModel(T2TModel* model);
void PrepareModel(Model* model);
};
}
......
source/sample/transformer/translate/T2TDataSet.cpp → source/sample/transformer/translate/DataSet.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -26,24 +25,26 @@
#include <fstream>
#include <algorithm>
#include "T2TDataSet.h"
#include "../module/T2TUtility.h"
#include "DataSet.h"
#include "../Utility.h"
using namespace transformer;
using namespace nmt;
namespace nts {
/* sort the output by id (in ascending order) */
void DataSet::SortInput() {
sort(inputBuffer.items, inputBuffer.items + inputBuffer.count, [](Example* a, Example* b) {
return a->values.count > b->values.count;
sort(inputBuffer.items, inputBuffer.items + inputBuffer.count,
[](Example* a, Example* b) {
return a->values.count > b->values.count;
});
}
/* sort the input by length (in descending order) */
void DataSet::SortOutput() {
sort(outputBuffer.items, outputBuffer.items + outputBuffer.count, [](Result* a, Result* b) {
return a->id < b->id;
sort(outputBuffer.items, outputBuffer.items + outputBuffer.count,
[](Result* a, Result* b) {
return a->id < b->id;
});
}
......@@ -74,7 +75,7 @@ void DataSet::LoadDataToBuffer()
: line.size() - indices[i];
string word = line.substr(indices[i], offset);
if (srcVocab.word2id.find(word) == srcVocab.word2id.end())
values.Add(3);
values.Add(UNK);
else
values.Add(srcVocab.word2id.at(word));
}
......@@ -100,7 +101,7 @@ void DataSet::LoadDataToBuffer()
}
/*
load a mini-batch to the device
load a mini-batch to the device (for translating)
>> batchEnc - a tensor to store the batch of input
>> paddingEnc - a tensor to store the batch of paddings
>> minSentBatch - the minimum number of sentence batch
......@@ -117,10 +118,10 @@ UInt64List DataSet::LoadBatch(XTensor* batchEnc, XTensor* paddingEnc,
size_t maxLen = inputBuffer[bufferUsed]->values.Size();
/* dynamic batching for sentences */
while ((realBatchSize < (inputBuffer.Size() - bufferUsed))
&& (realBatchSize * maxLen < batchSize)) {
realBatchSize++;
}
//while ((realBatchSize < (inputBuffer.Size() - bufferUsed))
// && (realBatchSize * maxLen < batchSize)) {
// realBatchSize++;
//}
/* real batch size */
if ((inputBuffer.Size() - bufferUsed) < realBatchSize) {
......@@ -133,13 +134,13 @@ UInt64List DataSet::LoadBatch(XTensor* batchEnc, XTensor* paddingEnc,
float* paddingValues = new float[realBatchSize * maxLen];
for (int i = 0; i < realBatchSize * maxLen; i++) {
batchValues[i] = 1;
paddingValues[i] = 0.0F;
batchValues[i] = PAD;
paddingValues[i] = 1.0F;
}
size_t cur = 0;
size_t curSrc = 0;
/* left padding */
/* right padding */
UInt64List infos;
size_t totalLength = 0;
......@@ -147,11 +148,11 @@ UInt64List DataSet::LoadBatch(XTensor* batchEnc, XTensor* paddingEnc,
infos.Add(inputBuffer[bufferUsed + i]->id);
totalLength += inputBuffer[bufferUsed + i]->values.Size();
cur = maxLen * (i + 1) - inputBuffer[bufferUsed + i]->values.Size();
for (int j = 0; j < inputBuffer[bufferUsed + i]->values.Size(); j++) {
batchValues[cur] = inputBuffer[bufferUsed + i]->values[j];
paddingValues[cur++] = 1.0F;
}
curSrc = maxLen * i;
for (int j = 0; j < inputBuffer[bufferUsed + i]->values.Size(); j++)
batchValues[curSrc++] = inputBuffer[bufferUsed + i]->values[j];
while (curSrc < maxLen * (i + 1))
paddingValues[curSrc++] = 0;
}
infos.Add(totalLength);
......@@ -178,7 +179,7 @@ the constructor of DataSet
void DataSet::Init(const char* dataFile, const char* srcVocabFN, const char* tgtVocabFN)
{
fp = new ifstream(dataFile);
CheckNTErrors(fp->is_open(), "can not open the file");
CheckNTErrors(fp->is_open(), "Can not open the test data");
bufferUsed = 0;
CheckNTErrors(strcmp(srcVocabFN, "") != 0, "missing source vocab file");
......
source/sample/transformer/translate/T2TDataSet.h → source/sample/transformer/translate/DataSet.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -26,7 +25,7 @@
#include <cstdio>
#include <vector>
#include <fstream>
#include "T2TVocab.h"
#include "Vocab.h"
#include "../../../tensor/XList.h"
#include "../../../tensor/XTensor.h"
......
source/sample/transformer/translate/T2TLengthPenalty.cpp → source/sample/transformer/translate/LengthPenalty.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -22,11 +21,11 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#include "T2TLengthPenalty.h"
#include "LengthPenalty.h"
using namespace nts;
namespace transformer
namespace nmt
{
/*
......@@ -36,7 +35,7 @@ where n = length of the sequence
>> alpha - the parameter controls the length preference
<< return - length penalty of the sequence
*/
float T2TLengthPenalizer::GNMT(float length, float alpha)
float LengthPenalizer::GNMT(float length, float alpha)
{
float base;
float lp;
......
source/sample/transformer/translate/T2TLengthPenalty.h → source/sample/transformer/translate/LengthPenalty.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -22,21 +21,21 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#ifndef __T2TLENGTHPENALTY_H__
#define __T2TLENGTHPENALTY_H__
#ifndef __LENGTHPENALTY_H__
#define __LENGTHPENALTY_H__
#include "../module/T2TUtility.h"
#include "../Utility.h"
#include "../../../tensor/XTensor.h"
using namespace nts;
namespace transformer
namespace nmt
{
/* We intend to penalize short sequences because they have higher score
in product of a sequence of probability-like terms and have more chances
to beat others in search. */
class T2TLengthPenalizer
class LengthPenalizer
{
public:
/* GNMT-like length penalty: pl = ((5 + n)/(5 + 1))^\alpha
......
source/sample/transformer/translate/T2TPredictor.cpp → source/sample/transformer/translate/Predictor.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -22,23 +21,23 @@
#include <iostream>
#include "T2TPredictor.h"
#include "../module/T2TNNUtil.h"
#include "Predictor.h"
#include "../module/NNUtil.h"
using namespace nts;
namespace transformer
namespace nmt
{
/* constructor */
T2TStateBundle::T2TStateBundle()
StateBundle::StateBundle()
{
states = NULL;
isStart = false;
}
/* de-constructor */
T2TStateBundle::~T2TStateBundle()
StateBundle::~StateBundle()
{
if (states != NULL)
delete[] states;
......@@ -48,18 +47,18 @@ T2TStateBundle::~T2TStateBundle()
create states
>> num - number of states
*/
void T2TStateBundle::MakeStates(int num)
void StateBundle::MakeStates(int num)
{
CheckNTErrors(num > 0, "invalid number");
if (states != NULL)
delete[] states;
states = new T2TState[num];
states = new State[num];
for (int i = 0; i < num; i++) {
states[i].prediction = -1;
states[i].pid = T2T_PID_EMPTY;
states[i].pid = _PID_EMPTY;
states[i].isEnd = false;
states[i].isStart = false;
states[i].isCompleted = false;
......@@ -74,26 +73,26 @@ void T2TStateBundle::MakeStates(int num)
}
/* constructor */
T2TPredictor::T2TPredictor()
Predictor::Predictor()
{
startSymbol = 2;
}
/* de-constructor */
T2TPredictor::~T2TPredictor()
Predictor::~Predictor()
{
}
/*
create an initial state
>> model - the t2t model
>> model - the model
>> top - the top-most layer of the network
>> input - input of the network
>> beamSize - beam size
>> state - the state to be initialized
*/
void T2TPredictor::Create(T2TModel* model, XTensor* top, const XTensor* input,
int beamSize, T2TStateBundle* state)
void Predictor::Create(Model* model, XTensor* top, const XTensor* input,
int beamSize, StateBundle* state)
{
int dims[MAX_TENSOR_DIM_NUM];
for (int i = 0; i < input->order - 1; i++)
......@@ -114,20 +113,20 @@ void T2TPredictor::Create(T2TModel* model, XTensor* top, const XTensor* input,
set start symbol
>> symbol - the symbol (in integer)
*/
void T2TPredictor::SetStartSymbol(int symbol)
void Predictor::SetStartSymbol(int symbol)
{
startSymbol = symbol;
}
/*
read a state
>> model - the t2t model that keeps the network created so far
>> model - the model that keeps the network created so far
>> state - a set of states. It keeps
1) hypotheses (states)
2) probabilities of hypotheses
3) parts of the network for expanding toward the next state
*/
void T2TPredictor::Read(T2TModel* model, T2TStateBundle* state)
void Predictor::Read(Model* model, StateBundle* state)
{
m = model;
s = state;
......@@ -147,9 +146,9 @@ predict the next state
>> needReorder - whether we need reordering the states
>> nstep - current time step of the target sequence
*/
void T2TPredictor::Predict(T2TStateBundle* next, XTensor& aliveState, XTensor& encoding,
XTensor& inputEnc, XTensor& paddingEnc, int batchSize, bool isStart,
XTensor& reorderState, bool needReorder, int nstep)
void Predictor::Predict(StateBundle* next, XTensor& aliveState, XTensor& encoding,
XTensor& inputEnc, XTensor& paddingEnc, int batchSize, bool isStart,
XTensor& reorderState, bool needReorder, int nstep)
{
int dims[MAX_TENSOR_DIM_NUM];
......@@ -221,14 +220,14 @@ void T2TPredictor::Predict(T2TStateBundle* next, XTensor& aliveState, XTensor& e
generate paths up to the states of the current step
>> state - state bundle of the current step
*/
XTensor T2TPredictor::GeneratePaths(T2TStateBundle* state)
XTensor Predictor::GeneratePaths(StateBundle* state)
{
CheckNTErrors(state->stateNum >= 0, "Illegal state!");
int distance = -1;
for (int i = 0; i < state->stateNum; i++) {
T2TState* cur = state->states + i;
State* cur = state->states + i;
int nsteps = 0;
while (cur != NULL) {
......@@ -245,7 +244,7 @@ XTensor T2TPredictor::GeneratePaths(T2TStateBundle* state)
path.SetZeroAll();
for (int i = 0; i < state->stateNum; i++) {
T2TState* cur = state->states + i;
State* cur = state->states + i;
int nsteps = 0;
while (cur != NULL) {
......@@ -263,21 +262,21 @@ get the predictions of the previous step
>> state - state bundle of the current step
>> devID - the device id for the predictions
*/
XTensor T2TPredictor::GetLastPrediction(T2TStateBundle* state, int devID)
XTensor Predictor::GetLastPrediction(StateBundle* state, int devID)
{
CheckNTErrors(state->stateNum >= 0, "Illegal state!");
IntList last;
for (int i = 0; i < state->stateNum; i++) {
T2TState* cur = state->states + i;
State* cur = state->states + i;
last.Add(cur->prediction);
}
XTensor lastPred;
InitTensor2D(&lastPred, last.Size(), 1, X_INT, devID);
lastPred.SetData(last.items, last.Size());
InitTensor2D(&lastPred, int(last.Size()), 1, X_INT, devID);
lastPred.SetData(last.items, int(last.Size()));
return lastPred;
}
......
source/sample/transformer/translate/T2TPredictor.h → source/sample/transformer/translate/Predictor.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -21,22 +20,22 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04
*/
#ifndef __T2TPREDICTOR_H__
#define __T2TPREDICTOR_H__
#ifndef __PREDICTOR_H__
#define __PREDICTOR_H__
#include "../T2TModel.h"
#include "T2TLengthPenalty.h"
#include "../Model.h"
#include "LengthPenalty.h"
using namespace std;
namespace transformer
namespace nmt
{
#define T2T_PID_EMPTY -1
#define _PID_EMPTY -1
/* state for search. It keeps the path (back-pointer), prediction distribution,
and etc. It can be regarded as a hypotheses in translation. */
class T2TState
class State
{
public:
/* we assume that the prediction is an integer */
......@@ -69,11 +68,11 @@ public:
int nstep;
/* pointer to the previous state */
T2TState* last;
State* last;
};
/* a bundle of states */
class T2TStateBundle
class StateBundle
{
public:
/* predictions */
......@@ -98,7 +97,7 @@ public:
float nstep;
/* list of states */
T2TState* states;
State* states;
/* number of states */
int stateNum;
......@@ -108,10 +107,10 @@ public:
public:
/* constructor */
T2TStateBundle();
StateBundle();
/* de-constructor */
~T2TStateBundle();
~StateBundle();
/* create states */
void MakeStates(int num);
......@@ -122,14 +121,14 @@ public:
we get the state of previous words and then generate the next word.
Here, a state can be regarded as the representation of words (word
indices, hidden states, embeddings and etc.). */
class T2TPredictor
class Predictor
{
private:
/* pointer to the transformer model */
T2TModel* m;
Model* m;
/* current state */
T2TStateBundle* s;
StateBundle* s;
/* start symbol */
int startSymbol;
......@@ -139,30 +138,30 @@ private:
public:
/* constructor */
T2TPredictor();
Predictor();
/* de-constructor */
~T2TPredictor();
~Predictor();
/* create an initial state */
void Create(T2TModel* model, XTensor* top, const XTensor* input, int beamSize, T2TStateBundle* state);
void Create(Model* model, XTensor* top, const XTensor* input, int beamSize, StateBundle* state);
/* set the start symbol */
void SetStartSymbol(int symbol);
/* read a state */
void Read(T2TModel* model, T2TStateBundle* state);
void Read(Model* model, StateBundle* state);
/* predict the next state */
void Predict(T2TStateBundle* next, XTensor& aliveIndices, XTensor& encoding,
void Predict(StateBundle* next, XTensor& aliveIndices, XTensor& encoding,
XTensor& inputEnc, XTensor& paddingEnc, int rawBatchSize,
bool isStart, XTensor& reorderState, bool needReorder, int nstep);
/* generate paths up to the states of the current step */
XTensor GeneratePaths(T2TStateBundle* state);
XTensor GeneratePaths(StateBundle* state);
/* get the predictions of the previous step */
XTensor GetLastPrediction(T2TStateBundle* state, int devID);
XTensor GetLastPrediction(StateBundle* state, int devID);
};
}
......
source/sample/transformer/translate/T2TSearch.h → source/sample/transformer/translate/Search.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,15 +19,15 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04, 2020-06
*/
#ifndef __T2TSEARCH_H__
#define __T2TSEARCH_H__
#ifndef __SEARCH_H__
#define __SEARCH_H__
#include "../T2TModel.h"
#include "T2TPredictor.h"
#include "../Model.h"
#include "Predictor.h"
using namespace std;
namespace transformer
namespace nmt
{
/* The class organizes the search process. It calls "predictors" to generate
......@@ -42,7 +41,7 @@ private:
float alpha;
/* predictor */
T2TPredictor predictor;
Predictor predictor;
/* max length of the generated sequence */
int maxLength;
......@@ -88,28 +87,28 @@ public:
~BeamSearch();
/* initialize the model */
void Init(T2TConfig& config);
void Init(Config& config);
/* search for the most promising states */
void Search(T2TModel* model, XTensor& input, XTensor& padding, IntList* output, XTensor& score);
void Search(Model* model, XTensor& input, XTensor& padding, IntList* output, XTensor& score);
/* preparation */
void Prepare(int myBatchSize, int myBeamSize);
/* compute the model score for each hypotheses */
void Score(T2TStateBundle* prev, T2TStateBundle* beam);
void Score(StateBundle* prev, StateBundle* beam);
/* generate token indices via beam pruning */
void Generate(T2TStateBundle* prev, T2TStateBundle* beam);
void Generate(StateBundle* prev, StateBundle* beam);
/* expand the search graph */
void Expand(T2TStateBundle* prev, T2TStateBundle* beam, XTensor& reorderState);
void Expand(StateBundle* prev, StateBundle* beam, XTensor& reorderState);
/* collect hypotheses with ending symbol */
void Collect(T2TStateBundle* beam);
void Collect(StateBundle* beam);
/* fill the hypotheses heap with incomplete hypotheses */
void FillHeap(T2TStateBundle* beam);
void FillHeap(StateBundle* beam);
/* save the output sequences and score */
void Dump(IntList* output, XTensor* score);
......@@ -118,17 +117,17 @@ public:
bool IsEnd(int token);
/* check whether all hypotheses are completed */
bool IsAllCompleted(T2TStateBundle* beam);
bool IsAllCompleted(StateBundle* beam);
/* update the beam by pruning finished states */
void RemoveFinishedStates(T2TStateBundle* beam, XTensor& aliveEncoding,
void RemoveFinishedStates(StateBundle* beam, XTensor& aliveEncoding,
XTensor& aliveInput, XTensor& alivePadding, XTensor& aliveIdx);
/* set end symbols for search */
void SetEnd(const int* tokens, const int tokenNum);
/* make a mask to prevent duplicated entries in beam expansion for the first position */
XTensor MakeFirstMask(T2TStateBundle* beam);
XTensor MakeFirstMask(StateBundle* beam);
};
class GreedySearch
......@@ -136,7 +135,7 @@ class GreedySearch
private:
/* predictor */
T2TPredictor predictor;
Predictor predictor;
/* max length of the generated sequence */
int maxLength;
......@@ -164,10 +163,10 @@ public:
~GreedySearch();
/* initialize the model */
void Init(T2TConfig& config);
void Init(Config& config);
/* search for the most promising states */
void Search(T2TModel* model, XTensor& input, XTensor& padding, IntList* output);
void Search(Model* model, XTensor& input, XTensor& padding, IntList* output);
/* preparation */
void Prepare(int myBatchSize);
......
source/sample/transformer/translate/T2TTranslator.cpp → source/sample/transformer/translate/Translator.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -20,27 +19,25 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-04, 2020-06
*/
#include <cmath>
#include "T2TTranslator.h"
#include "T2TSearch.h"
#include "../module/T2TUtility.h"
#include "Search.h"
#include "Translator.h"
#include "../Utility.h"
#include "../../../tensor/XTensor.h"
#include "../../../tensor/XUtility.h"
#include "../../../tensor/core/CHeader.h"
using namespace nts;
namespace transformer
namespace nmt
{
/* constructor */
T2TTranslator::T2TTranslator()
Translator::Translator()
{
}
/* de-constructor */
T2TTranslator::~T2TTranslator()
Translator::~Translator()
{
if (beamSize > 1)
delete (BeamSearch*)seacher;
......@@ -49,7 +46,7 @@ T2TTranslator::~T2TTranslator()
}
/* initialize the model */
void T2TTranslator::Init(T2TConfig& config)
void Translator::Init(Config& config)
{
beamSize = config.beamSize;
vSize = config.srcVocabSize;
......@@ -58,17 +55,17 @@ void T2TTranslator::Init(T2TConfig& config)
wordBatch = config.wBatchSize;
if (beamSize > 1) {
XPRINT1(0, stderr, "Translating with beam search (%d)\n", beamSize);
LOG("translating with beam search (%d)", beamSize);
seacher = new BeamSearch();
((BeamSearch*)seacher)->Init(config);
}
else if (beamSize == 1) {
XPRINT1(0, stderr, "Translating with greedy search (%d)\n", beamSize);
LOG("translating with greedy search");
seacher = new GreedySearch();
((GreedySearch*)seacher)->Init(config);
}
else {
CheckNTErrors(false, "invalid beam size\n");
CheckNTErrors(false, "Invalid beam size\n");
}
}
......@@ -80,8 +77,8 @@ test the model
>> ofn - output data file
>> model - pretrained model
*/
void T2TTranslator::Translate(const char* ifn, const char* sfn, const char* tfn,
const char* ofn, T2TModel* model)
void Translator::Translate(const char* ifn, const char* sfn,
const char* tfn, const char* ofn, Model* model)
{
int wc = 0;
int wordCountTotal = 0;
......@@ -99,8 +96,7 @@ void T2TTranslator::Translate(const char* ifn, const char* sfn, const char* tfn,
XTensor paddingEnc;
batchLoader.Init(ifn, sfn, tfn);
XPRINT1(0, stderr, "[INFO] loaded the input file, elapsed=%.1fs \n",
GetClockSec() - startT);
LOG("loaded the input file, elapsed=%.1fs ", GetClockSec() - startT);
int count = 0;
double batchStart = GetClockSec();
......@@ -130,24 +126,24 @@ void T2TTranslator::Translate(const char* ifn, const char* sfn, const char* tfn,
for (int i = 0; i < indices.Size() - 1; ++i) {
Result* res = new Result;
res->id = indices[i];
res->id = int(indices[i]);
res->res = output[i];
batchLoader.outputBuffer.Add(res);
}
delete[] output;
wc += indices[-1];
wordCountTotal += indices[-1];
wc += int(indices[-1]);
wordCountTotal += int(indices[-1]);
sentCount += (indices.Size() - 1);
sentCount += int(indices.Size() - 1);
batchCount += 1;
if (count % 1 == 0) {
double elapsed = GetClockSec() - batchStart;
batchStart = GetClockSec();
XPRINT3(0, stderr, "[INFO] elapsed=%.1fs, sentence=%f, sword=%.1fw/s\n",
elapsed, float(sentCount) / float(batchLoader.inputBuffer.Size()),
double(wc) / elapsed);
LOG("elapsed=%.1fs, sentence=%f, sword=%.1fw/s",
elapsed, float(sentCount) / float(batchLoader.inputBuffer.Size()),
double(wc) / elapsed);
wc = 0;
}
}
......@@ -169,8 +165,8 @@ void T2TTranslator::Translate(const char* ifn, const char* sfn, const char* tfn,
double elapsed = GetClockSec() - startDump;
XPRINT2(0, stderr, "[INFO] translation completed (word=%d, sent=%ld)\n",
wordCountTotal, batchLoader.inputBuffer.Size() + batchLoader.emptyLines.Size());
LOG("translation completed (word=%d, sent=%zu)",
wordCountTotal, batchLoader.inputBuffer.Size() + batchLoader.emptyLines.Size());
}
/*
......@@ -178,7 +174,7 @@ dump the result into the file
>> file - data file
>> output - output tensor
*/
void T2TTranslator::Dump(FILE* file, XTensor* output)
void Translator::Dump(FILE* file, XTensor* output)
{
if (output != NULL && output->unitNum != 0) {
int seqLength = output->dimSize[output->order - 1];
......
source/sample/transformer/translate/T2TTranslator.h → source/sample/transformer/translate/Translator.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2020, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -21,17 +20,17 @@
* $Modified by: HU Chi (huchinlp@gmail.com) 2020-06
*/
#ifndef __T2TTESTER_H__
#define __T2TTESTER_H__
#ifndef __TESTER_H__
#define __TESTER_H__
#include "T2TSearch.h"
#include "T2TDataSet.h"
#include "Search.h"
#include "DataSet.h"
namespace transformer
namespace nmt
{
/* This class translates test sentences with a trained model. */
class T2TTranslator
class Translator
{
public:
/* vocabulary size of the source side */
......@@ -57,17 +56,17 @@ public:
public:
/* constructor */
T2TTranslator();
Translator();
/* de-constructor */
~T2TTranslator();
~Translator();
/* initialize the model */
void Init(T2TConfig& config);
void Init(Config& config);
/* test the model */
void Translate(const char* ifn, const char* vfn, const char* ofn,
const char* tfn, T2TModel* model);
const char* tfn, Model* model);
/* dump the result into the file */
void Dump(FILE* file, XTensor* output);
......
source/sample/transformer/translate/T2TVocab.cpp → source/sample/transformer/translate/Vocab.cpp
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2018, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -21,8 +20,8 @@
#include <fstream>
#include "T2TVocab.h"
#include "../module/T2TUtility.h"
#include "Vocab.h"
#include "../Utility.h"
namespace nts {
......@@ -31,7 +30,7 @@ void Vocab::Load(const string& src)
{
string vsz, sid;
ifstream f(src, ios::in);
CheckNTErrors(f.is_open(), "Unable to open the vocabulary file");
CheckNTErrors(f.is_open(), "unable to open the vocabulary file");
/* get the vocab size and the start id */
f >> vsz >> sid;
......
source/sample/transformer/translate/T2TVocab.h → source/sample/transformer/translate/Vocab.h
/* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2018, Natural Language Processing Lab, Northeastern University.
* All rights reserved.
/* NiuTrans.NMT - an open-source neural machine translation system.
* Copyright (C) 2020 NiuTrans Research. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
......@@ -19,8 +18,8 @@
* $Created by: HU Chi (huchinlp@foxmail.com) 2020-01-03
*/
#ifndef __T2TVOCAB_H__
#define __T2TVOCAB_H__
#ifndef __VOCAB_H__
#define __VOCAB_H__
#include <cstdio>
#include <unordered_map>
......@@ -30,10 +29,10 @@ using namespace std;
namespace nts {
/* user-defined symbols */
#define UNK 0
#define PAD 1
#define SOS 2
#define EOS 2
#define UNK 3
/* the vocabulary class */
struct Vocab
......
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