Scaling up Deep Learning by Scaling Down

Scaling up deep
learning by scaling
down
—
Nick Pentreath
Principal Engineer
@MLnick

About
IBM Developer / © 2020 IBM Corporation
– @MLnick on Twitter, Github, LinkedIn
– Principal Engineer, IBM CODAIT (Center for
Open-Source Data & AI Technologies)
– Machine Learning & AI
– Apache Spark committer & PMC
– Author of Machine Learning with Spark
– Various conferences & meetups
2

Improving the Enterprise AI Lifecycle in Open Source
IBM Developer / © 2020 IBM Corporation 3
– CODAIT aims to make AI solutions
dramatically easier to create,
deploy, and manage in the
enterprise.
– We contribute to and advocate for
the open-source technologies that
are foundational to IBM’s AI
offerings.
– 30+ open-source developers!
Center for Open Source Data & AI Technologies
codait.org
CODAIT
Open Source @ IBM

Agenda
4
– Deep Learning overview & computational
considerations
– Evolving efficiency of model architectures
– Model compression
– Model distillation
– Conclusion
DEG / June 4, 2020 / © 2020 IBM Corporation

Machine Learning
Workflow
5
Data Analyze Process Train Deploy
Predict
&
Maintain
DEG / June 4, 2020 / © 2020 IBM Corporation
Compute-heavy

Deep Learning
– Original theory from 1940s; computer
models originated around 1960s; fell out of
favor in 1980s/90s
– Recent resurgence due to
• Bigger (and better) data; standard datasets
(e.g. ImageNet)
• Better hardware (GPUs, TPUs)
• Improvements to algorithms, architectures and
optimization
– Leading to new state-of-the-art results in
computer vision (images and video);
speech/text; language translation and
more
IBM Developer / © 2020 IBM Corporation 6Source: Wikipedia

Modern Neural Networks
– Deep (multi-layer) networks
– Computer vision
• Convolution neural networks (CNNs)
• Image classification, object detection,
segmentation
– Sequences and time-series
• Machine translation, text generation
• Recurrent neural networks - LSTM, GRU
– Natural language processing
• Word embeddings
• Transformers, attention mechanisms
– Deep learning frameworks
• Flexibility, computation graphs, auto-
differentiation, GPUs
IBM Developer / © 2020 IBM Corporation 7Source: Stanford CS231n

Evolution of Training
Computation Requirements
Source
Computational
resources
required for
training AI
models doubles
every 3 to 4
months

Example: Image
Classification
IBM Developer / © 2020 IBM Corporation 9Source
Input image Inference Prediction
beagle: 0.82
basset: 0.09
bluetick: 0.07
...

Example: Inception V3
Source
Effectively
matrix
multiplication
~24 million
parameters
78.8%
accuracy
(ImageNet)

Accuracy vs Computational
Complexity (ImageNet)
Source: Paper, blog

Computational efficiency
(ImageNet)
Source: Paper, blog

Deep Learning
Deployment
– Model training typically
uses substantial
hardware
– GPU / multi-GPU
– Cloud-based
deployment scenarios

Deep Learning
Deployment
– Edge devices have more limited resources
• Memory
• Compute (CPU, mobile GPU, edge TPU)
• Network bandwidth
– Also applies to low-latency applications

How do we improve
performance
efficiency?
– Architecture
improvements
– Model pruning
– Quantization
– Model distillation

Architecture
Improvements

Specialized architectures for
low-resource targets
Source
Standard
Convolution
Building Block
Inception V3 MobileNet V1
Depthwise
Convolution
Building Block
(~8x less
computation)
~4 million
parameters
70.9%
accuracy
~24 million
parameters
78.8%
accuracy
(ImageNet)

Trade off accuracy vs model size
Source
– Scale layer width &
resolution multiplier to
target available
computation budget
– Width multiplier =
“thinner” models
– Resolution multiplier
scales input image
representation

MobileNet V2
Source
– Same depthwise
convolution backbone
– Add linear bottlenecks
& shortcut connections
~3.4 million
parameters
72%
accuracy

Accuracy vs Computation - Updated
(ImageNet)
Source: Paper, blog

Computational efficiency - Updated
(ImageNet)
Source: Paper, blog

EfficientNet
Source: blog post, paper
– Neural Architecture
Search to find backbone
– Optimize for accuracy &
efficiency (FLOPS)
~5.3 million
parameters
77.3%
accuracy
~60 million
parameters
84.5%
accuracy

MobileNet V3
Source: GitHub, paper
– Hardware-aware Neural
Architecture Search
~5.4 million
parameters
75.2%
accuracy

One network to rule them all?
Source: GitHub, paper
– Once for All: Train One
Network and Specialize
it for Efficient
Deployment
– Manual design or NAS is
hugely costly in terms of
computation
– Train one network,
“cherry-pick” the sub-
net without additional
training

Model Pruning
– Reduce # of model
parameters
– Effectively like L1
regularization – remove
weights with small
impact on prediction
– Sparse weights ->
model compression &
lower latency

Model Pruning
Source
70
71
72
73
74
75
76
77
78
79
0% 20% 40% 60% 80% 100%
Top-1Accuracy(%)
Model Sparsity
ImageNet Classification
InceptionV3
MobileNet V1 224

Model Pruning
Source
26
26.5
27
27.5
28
28.5
29
29.5
30
0% 20% 40% 60% 80% 100%
BLEUScore
Model Sparsity
Language Translation
English-German
German-English

Quantization

Quantization
Source
– Most DL computation
users 32 (or even 64)
bits floating point
– Quantization reduces
numerical precision of
weights by binning
values
– Popular targets are 16-
bit FP and 8-bit integer
coding

Quantization
Source
– Post-training quantization
• Useful if you can’t (or don’t wish
to) retrain a model
• Give up accuracy
• Various options
– Float16
– Dynamic
– Int8
– Training-aware quantization
• Much more complex
• Can provide large efficiency
gains with minimal accuracy loss
78
71.9
77.2
63.7
77.5
70.9
InceptionV3 MobileNet V2 224Top-1Accuracy(%)
Original Post-training Training-aware

Quantization
Source
100% 100%
25% 26%
InceptionV3 MobileNet V2 224
%orginalmodelsize
Original Quantized
75%
110%
48%
61%
InceptionV3 MobileNet V2 224
%orginallatency
Post-training Training-aware

Quantization
– TensorFlow Model
Optimization
– PyTorch
– Distiller for PyTorch

Model Distillation
– Large models may be
over-parameterized
– Use a large, complex
model to teach a
smaller, simpler model
– Effectively distil the
core knowledge of the
large model

Model Distillation
Source: Distiller docs, paper

Model Distillation
– BERT model distillations have been very
successful
– DistilBERT
– TinyBERT
– Others (see this blog post)

Conclusion
– Model distillation is less popular but
potentially compelling in NLP tasks
– Area of rapid research evolution
– New efficient model architectures are
rapidly evolving
• If one fits your needs, use it!
– Compression techniques can yield
large efficiency gains
• Now good support in DL frameworks
/ supporting libraries
– Perhaps combining pruning &
quantization (though trickier)
36DEG / June 4, 2020 / © 2020 IBM Corporation

Thank you
codait.org
twitter.com/MLnick
github.com/MLnick
developer.ibm.com
Check out the Model Asset Exchange
https://ibm.biz/model-exchange
Sign up for IBM Cloud
https://ibm.biz/BdqdSi

References
Efficient Inference in Deep Learning – Where
is the Problem?
Analysis of deep neural networks
MobileNets
EfficientNet
Making Neural Nets Work With Low Precision
Speeding up BERT
Distilling the Knowledge in a Neural Network
Once for All: Train One Network and
Specialize it for Efficient Deployment
Distiller – PyTorch
TensorFlow Model Optimization
Deep Compression: Compressing Deep
Neural Networks with Pruning, Trained
Quantization and Huffman Coding

Scaling up Deep Learning by Scaling Down

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