[Impl] neural machine translation

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This document summarizes the key aspects of a neural machine translation model using sequence-to-sequence with attention. It describes the encoder-decoder architecture using stacked LSTMs, dot product attention, and how the model is trained on WMT'14 English-German data and evaluated on Newstest2013 data. It also provides information on the tokenizer, dataset creation, model configuration, and training utilities.

[IMPL] Neural Machine Translation
장재호
19. 09. 06 (금)

목차
• Sequence to sequence with attention
• Data
• Tokenizer
• NMTDataset
• Model
• EncLayer
• DecCell
• DecLayer
• Utils

Sequence to sequence with
attention
참고자료
“Effective Approaches to Attention-based Neural Machine Translation”. Luong et al. 2015.
IBM/pytorch-seq2seq: https://github.com/IBM/pytorch-seq2seq

Sequence to sequence with attention
• Model: Sequence to sequence with attention (Luong et al.
2015)
• Tokenizer (SentencePiece):
• BPE (Byte Pair Encoding)
• WPM (WordPieceModel)
• Attention
• Dot Product Attention
• Multiplicative Attention
• Additive Attention

Sequence to sequence with attention
• Encoder-Decoder
• Encoder: Stacked Bidirectional
LSTM
• Decoder: Stacked LSTM

Sequence to sequence with attention
• Attention
• Dot Product Attention
• Multiplicative Attention
• Additive Attention

Data
Train: WMT’14 English-German Data
Test: Newstest2013 English-German Data
데이터 출처: https://nlp.stanford.edu/projects/nmt/
SentencePiece: https://github.com/google/sentencepiece

Data
• SentencePiece
• Subword Segmentation Library
• Pytorch 데이터 유틸리티
• torch.utils.data.Dataset
• Abstract class
• Iterable object
• __len__()
• __getitem__()
• torch.utils.data.Dataloader
• Generator object
• Collate (callback function)

Data
• https://github.com/om00839/Seq2Seq/blob/master/Script%20
-%20data.ipynb

Model
참고자료
“Effective Approaches to Attention-based Neural Machine Translation”. Luong et al. 2015.
IBM/pytorch-seq2seq: https://github.com/IBM/pytorch-seq2seq

Model
• Layers
• EncLayer
• DecCell
• DecLayer
• Attention
• BaseAttention
• DotProdAttention
• MulAttention
• AddAttention
• Model
• Seq2SeqWithAttn

Model
• EncLayer
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LSTM Layer
Embedding Layer
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Model
• DecCell
DecCell
Embedding
LSTM
Cell𝑤+
(+) Linear
Classifer 𝑙𝑜𝑔𝑖𝑡+Attention
𝑠+2#,
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Source Input
OutputTarget

Model
• DecLayer
• train
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eos
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Source Input
OutputTarget

Model
• DecLayer
• infer
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bos
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Source Input
OutputTarget

Model
• Configuration
• src_vocab_size=32000
• tar_vocab_size=32000
• attention=AddAttention
• embedding_dim=256
• hidden_dim=256
• enc_hidden_dim = 256
• dec_hidden_dim = 256*2 (enc_hidden_dim * n_directions)
• n_layers=2,
• bidirectional=True

Model
• https://github.com/om00839/Seq2Seq/blob/master/Script%20
-%20model.ipynb

Utils
참고자료
IBM/pytorch-seq2seq: https://github.com/IBM/pytorch-seq2seq

Utils
• Train & evaluate
• train_batches()
• evaluate_batches()
• generate_sentence()
• EarlyStopping
• Metrics (pytorch-nlp)
• bleu_score
• Accuracy

Utils
• https://github.com/om00839/Seq2Seq/blob/master/Script%20
-%20utils.ipynb

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ahzuo

UIUC毕业证offer【微信95270640】☀《伊利诺伊大学|厄巴纳-香槟分校毕业证购买》GoogleQ微信95270640《UIUC毕业证模板办理》加拿大文凭、本科、硕士、研究生学历都可以做,二、业务范围： ★、全套服务：毕业证、成绩单、化学专业毕业证书伪造《伊利诺伊大学|厄巴纳-香槟分校大学毕业证》Q微信95270640《UIUC学位证书购买》 (诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡 1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书； 2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。 3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。办理伊利诺伊大学|厄巴纳-香槟分校伊利诺伊大学|厄巴纳-香槟分校毕业证offer流程： 1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）； 2开始安排制作毕业证成绩单电子图； 3毕业证成绩单电子版做好以后发送给您确认； 4毕业证成绩单电子版您确认信息无误之后安排制作成品； 5成品做好拍照或者视频给您确认； 6快递给客户（国内顺丰国外DHLUPS等快读邮寄） -办理真实使馆公证（即留学回国人员证明） -办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度） -全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！（详情请加一下文凭顾问+微信:95270640）欢迎咨询！的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下须绕过高楼旁边的垃圾堆下八个台阶才到父亲的家很狭小除了一张单人床和一张小方桌几乎没有多余的空间山娃一下子就联想起学校的男小便处山娃很想笑却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气间

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GetInData

Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots. In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms. Why do we need yet another (open-source ) Copilot? How can we build one? Architecture and evaluation

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Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is Multiply with different modes (map) 1. Performance of sequential execution based vs OpenMP based vector multiply. 2. Comparing various launch configs for CUDA based vector multiply. Sum with different storage types (reduce) 1. Performance of vector element sum using float vs bfloat16 as the storage type. Sum with different modes (reduce) 1. Performance of sequential execution based vs OpenMP based vector element sum. 2. Performance of memcpy vs in-place based CUDA based vector element sum. 3. Comparing various launch configs for CUDA based vector element sum (memcpy). 4. Comparing various launch configs for CUDA based vector element sum (in-place). Sum with in-place strategies of CUDA mode (reduce) 1. Comparing various launch configs for CUDA based vector element sum (in-place).

Ch03-Managing the Object-Oriented Information Systems Project a.pdf

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一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理

dwreak4tg

原版定制【微信:41543339】【(BCU毕业证书)伯明翰城市大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

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TravisMalana

一比一原版(UofS毕业证书)萨省大学毕业证如何办理

v3tuleee

原版定制【微信:41543339】【(UofS毕业证书)萨省大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

Subhajit Sahu

For massive graphs that fit in RAM, but not in GPU memory, it is possible to take advantage of a shared memory system with multiple CPUs, each with multiple cores, to accelerate pagerank computation. If the NUMA architecture of the system is properly taken into account with good vertex partitioning, the speedup can be significant. To take steps in this direction, experiments are conducted to implement pagerank in OpenMP using two different approaches, uniform and hybrid. The uniform approach runs all primitives required for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid approach runs certain primitives in sequential mode (i.e., sumAt, multiply).

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[Impl] neural machine translation

1. [IMPL] Neural Machine Translation 장재호 19. 09. 06 (금)

2. 목차 • Sequence to sequence with attention • Data • Tokenizer • NMTDataset • Model • EncLayer • DecCell • DecLayer • Utils

3. Sequence to sequence with attention 참고자료 “Effective Approaches to Attention-based Neural Machine Translation”. Luong et al. 2015. IBM/pytorch-seq2seq: https://github.com/IBM/pytorch-seq2seq

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5. Sequence to sequence with attention • Encoder-Decoder • Encoder: Stacked Bidirectional LSTM • Decoder: Stacked LSTM

6. Sequence to sequence with attention • Attention • Dot Product Attention • Multiplicative Attention • Additive Attention

7. Data Train: WMT’14 English-German Data Test: Newstest2013 English-German Data 데이터 출처: https://nlp.stanford.edu/projects/nmt/ SentencePiece: https://github.com/google/sentencepiece

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12. Model • EncLayer 𝒉 LSTM Layer Embedding Layer 𝑤# (%) 𝑤' (%) 𝑤() (%) … ℎ# ℎ' ℎ()… Source Input OutputTarget

13. Model • DecCell DecCell Embedding LSTM Cell𝑤+ (+) Linear Classifer 𝑙𝑜𝑔𝑖𝑡+Attention 𝑠+2#, 𝑐+2# 𝑠+, 𝑐+ 𝒉 Source Input OutputTarget

14. Model • DecLayer • train 𝐷𝑒𝑐 𝐶𝑒𝑙𝑙# 𝑤# (+) bos 𝑤' (+) 𝑤(82# (+) … 𝑙𝑜𝑔𝑖𝑡# …𝑙𝑜𝑔𝑖𝑡' 𝑙𝑜𝑔𝑖𝑡(8 𝐷𝑒𝑐 𝐶𝑒𝑙𝑙' 𝐷𝑒𝑐 𝐶𝑒𝑙𝑙(8 𝑤' (+) 𝑤9 (+) 𝑤(8 (+) eos … Cross Entropy … Source Input OutputTarget

15. Model • DecLayer • infer 𝐷𝑒𝑐 𝐶𝑒𝑙𝑙# 𝑤# (+) bos :𝑤' (+) :𝑤(82# (+) … 𝑙𝑜𝑔𝑖𝑡# …𝑙𝑜𝑔𝑖𝑡' 𝑙𝑜𝑔𝑖𝑡(8 𝐷𝑒𝑐 𝐶𝑒𝑙𝑙' 𝐷𝑒𝑐 𝐶𝑒𝑙𝑙(8 :𝑤' (+) :𝑤9 (+) :𝑤(8 (+) eos … ℎ() , 𝑐() … Source Input OutputTarget

16. Model • Configuration • src_vocab_size=32000 • tar_vocab_size=32000 • attention=AddAttention • embedding_dim=256 • hidden_dim=256 • enc_hidden_dim = 256 • dec_hidden_dim = 256*2 (enc_hidden_dim * n_directions) • n_layers=2, • bidirectional=True

17. Model • https://github.com/om00839/Seq2Seq/blob/master/Script%20 -%20model.ipynb

18. Utils 참고자료 IBM/pytorch-seq2seq: https://github.com/IBM/pytorch-seq2seq

19. Utils • Train & evaluate • train_batches() • evaluate_batches() • generate_sentence() • EarlyStopping • Metrics (pytorch-nlp) • bleu_score • Accuracy

20. Utils • https://github.com/om00839/Seq2Seq/blob/master/Script%20 -%20utils.ipynb

21. 감사합니다

[Impl] neural machine translation

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