Pluto A Distributed Heterogeneous Deep Learning Framework. Jun Yang, Yan Chen Large Scale Learning, Alibaba Cloud

Transcription

1 Pluto A Distributed Heterogeneous Deep Learning Framework Jun Yang, Yan Chen Large Scale Learning, Alibaba Cloud

2 Outline PAI(Platform of Artificial Intelligence) PAI Overview Deep Learning with PAI Pluto PAI DL Application Chatbot Engine Summary 2

3 Machine Learning Platforms 3

4 PAI Overview Frontend PAI WEB Console PAI IDE Algorithms Feature Engineering Statistic Methods Machine Learning Deep Learning Distributed Computing PAI SDK MR/MPI/PS/Graph/Pluto Fuxi Scheduler CPU/GPU/FPGA/ASIC/ Serving Data Storage OSS Database: ODPS/RDS Streaming data: DataHub/TT/Kafka Tutorial: data.aliyun.com 4

5 PAI Project Search Experiment Data Source Component Model Serving 5

6 Machine Learning with PAI Data Preprocessing Feature Engineering Statistics Modeling Deep Learning Application Sampling & Filtering Data Merge Fill Missing Values Feature Transformatio n Feature Selection Feature Importance Correlation Coefficients Histogram Hypothesis Test Binary Classificatio n Multiple Classificatio n Clustering DNN CNN RNN Pluto NLP Search & Rec. Image Process Normalizatio n Feature Generation Visualization Regression A La Carte Network Analysis Prediction Financial Section Evaluation 6

7 Deep Learning with PAI 7

8 PAI TensorFlow Rich Data IO Distributed Job Optimization (Multi. GPU/CPUs) Easy model Serving Hyper Parameter Tuning 8

9 Pluto 9

10 Single-card Optimization Compiler-oriented strategy Fuse small ops into bigger one Reduce CUDA kernel launch overhead Prepare data layout friendly with low-level computation library Memory optimization Here again compiler-oriented tactics Dependency analysis Lifetime analysis 10

11 Multi-cards Optimization Heuristic-based Model Parallelism Both model weights and feature map taken into consideration Memory allocator strategy taken into consideration A greedy allocation algorithm With pre-run support 11

12 Multi-cards Optimization Hybrid-parallelism Mixture of data-parallelism and model-parallelism For communication-intensive parts, consider model-parallelism For computation-intensive parts, consider data-parallelism Tricks Integrate seamlessly with computation graph style Happier with pyramid network 12

13 Multi-cards Optimization Hybrid-parallelism(cont.) M40 Result K40 Result 13

14 Multi-cards Optimization Late-multiply Customized for fully-connected layers Trade-off between computation and communication W avg : [N l,n l+1 ], X:[M, N l ], E:[M, N l+1 ], here N l,n l+1 layer sizes, M is mini-batch size 14

15 Multi-cards Optimization Late-multiply(cont.) 15

16 Multi-cards Optimization Heuristic-based MA Automatic batch-size selection Learning rate auto-tuning Happier with sequential model 16

17 Multi-cards Optimization Heuristic-based MA(cont.) Model Metrics Training Time in Wallclock 17

18 Inference Optimization Quantization Significantly reduce model size(4x) Around 2X speed-up on average Binarized Neural Network Binarize model weights Convert floating point computation into bit manipulation Both model size and computation speed significantly improved Training process needs to be manipulated to compensate for accuracy Happier with CNN, but for RNN 18

19 PAI DL Application 19

20 AliMe Personal Assistant Bot in E-commerce AliMe for Customers AliMe for Sellers AliMe for Enterprises From 云栖大会 20

21 Open-Domain Conversations Retrieval Model Learning to rank Query QA pairs Knowledge Base Q 1 -A 1 : s 1 Q 2 -A 2 : s 2 Q 3 -A 3 : s 3... Q n -A n : s n A1 Generation Model Sequence to Sequence (Seq2Seq) Model Cho et al., 2014 Recurrent Neural Networks: LSTM, GRU (our choice) 21

22 A Hybrid Conversation Model based on Seq2Seq Overview Query IR Candidates Answer Rerank Score > T Yes Output No Chat logs SNS data QA pairs KnowledgeBase Seq2Seq Model Answer Generation Retrieval Module Seq2Seq Based Rerank and Generation Modules [AliMe Chat: Minghui Qiu et al., ACL 2017] 22

23 PAI DL Support for AliMe Both the offline training and online serving backed by PAI Through heuristic-based MA, the offline training task has 2.8X convergence speed-up with 4 cards setting Through quantization, the online serving task has 1.5X speed-up on commodity CPU servers. 23

24 Conclusion PAI DL End2end machine learning platform Support big data analytics Optimized Deep learning algorithms Scheduling on CPU/GPU cloud More data intelligence Pluto Distributed optimization engine of PAI DL PAI DL Application PAI DL makes it easy to build DL methods for industrial applications SCAN BARCODE! START YOUR TRIAL! 24

25 We are hiring! J muzhuo.yj@alibaba-inc.com chenyan.cy@alibaba-inc.com 25

26 Reference AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine, Minghui Qiu et al., ACL Deep Learning with PAI: a Case Study of AliMe, Minghui Qiu et al., Deep Learning Summit TensorFlow in AliMe, Jun Yang et al., Shanghai GDG Mar.,

27 Thanks!