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# GeometricFlux.jl: Geometric Deep Learning on Flux

### Yueh-Hua Tu @ COSCUP 2020

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# Why graph neural network?

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# Nowadays AI utilizes vector/matrix to represent data

.center[![](https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/nowadays-ai.svg)]

* Computer vision
* Natural language processing
* Speech
* Biomedical signals

# Many scientific data use graph structure

.center[![](https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/graph-ai.svg)]

* Social networks in socail science
* Traffic networks
* Gene regulatory networks in biology
* Molecular structure in chemistry
* Knowledge graph 
* 3D object surfaces in computer graphics

# Human gene coexpression network

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/human_gene.jpg" width="70%">]

> [picture source](http://bioinfow.dep.usal.es/coexpression/)

# Knowledge graph

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/the-semantic-knowledge-graph.jpg" width="70%">]

> [picture source](https://www.slideshare.net/treygrainger/the-semantic-knowledge-graph)

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# Graphs are more representative than vectors

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/graph-signal.svg" width="90%">]

# Graphs are discrete approximation to manifold

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/manifold.png" width="50%">]

.footnote[[Geometric deep learning: going beyond Euclidean data](https://arxiv.org/abs/1611.08097)]

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## Geometric Deep Learning

## = Graph/manifold + Deep Learning

# = Powerful!

.footnote[[Geometric deep learning: going beyond Euclidean data](https://arxiv.org/abs/1611.08097)]

# GeometricFlux.jl

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/logo.svg" width="50%">]

.footnote[https://github.com/yuehhua/GeometricFlux.jl]

# Features

* Extend **Flux** deep learning framework in Julia
* Support of CUDA GPU with **CuArrays**
* Integrate with existing **JuliaGraphs** ecosystem
* Support generic graph neural network architectures
* (WIP) Integrate GNN benchmark datasets

# Graph convolutional layers

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/gcn.svg" width="90%">]

```julia
GCNConv(adj_mat, num_features=>hidden, relu)
```

> [A Comprehensive Survey on Graph Neural Networks](https://arxiv.org/abs/1901.00596)

# Use it as you use Flux

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/flux-logo.png" width="50%">]

```julia
model = Chain(GCNConv(g, 5120=>1000, relu),
              GCNConv(g, 1000=>500, relu),
              softmax)

## Loss
loss(x, y) = crossentropy(model(x), y)
accuracy(x, y) = mean(onecold(model(x)) .== onecold(y))

## Training
ps = Flux.params(model)
train_data = [(train_X, train_y)]
opt = ADAM(0.01)
evalcb() = @show(accuracy(train_X, train_y))

Flux.train!(loss, ps, train_data, opt, cb=throttle(evalcb, 10))
```

# Compatible with layers in Flux

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/flux-logo.png" width="50%">]

```julia
## Model
model = Chain(GCNConv(g, 5120=>1000, relu),
              Dropout(0.5),
              GCNConv(g, 1000=>500, relu),
              Dense(500, 7),
              softmax)
```

# Layers

### Convolution layers

* GCNConv
* GraphConv
* ChebConv
* GatedGraphConv
* GATConv
* EdgeConv

### Pooling layers

* GlobalPool
* TopKPool
* MaxPool
* MeanPool
* sum/sub/prod/div/max/min/mean pool

# Special layers for autoencoders

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/vgae.svg" width="100%">]

```julia
GAE(enc, InnerProductDecoder(σ))
VGAE(VariationalEncoder(enc, h_dim, z_dim), InnerProductDecoder(σ))
```

* VariationalEncoder (WIP)
* InnerProductDecoder

# Construct layers from SimpleGraph/SimpleWeightedGraph

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/juliagraphs.png" width="30%">]

It allows you to cache a *static* graph in layer for computational efficiency.

```julia
g = SimpleGraph(5)
add_edge!(1, 2); add_edge!(3, 4)

GCNConv(g, num_features=>1000, relu)
```

# AD-ready with Zygote

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/zygote-logo.png" width="50%">]

GeometricFlux support AD with Zygote on CPU and GPU!

# Use message passing scheme

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/MPNN-2.svg" width="50%">]

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/MPNN.png" width="30%">]

* $h_v^t$: vertex features
* $e_{uv}$: edge features
* $N(v)$: neighbors of vertex $v$

# Message passing scheme API

#### Define your message-passing layer

```julia
struct MyMessagePassingLayer <: MessagePassing
    ...
end
```

#### Override your message function

```julia
message(mp::T, x_i, x_j, e_ij) where {T<:MessagePassing} = x_j
```

#### Choose your aggregation function

```julia
aggregate_neighbors(mp::T, aggr::Symbol, ...) where {T<:MessagePassing}
```

#### Override your update function

```julia
update(mp::T, m, x) where {T<:MessagePassing} = m
```

# Graph network

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/gn-5.svg" width="100%">]

.footnote[[Relational inductive biases, deep learning, and graph networks](https://arxiv.org/abs/1806.01261)]

# Define your own graph network block as layer

```julia
struct MyGNBlock <: GraphNet
    ...
end
```

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/gn-4.svg" width="100%">]

```julia
Chain(MyGNBlock(...),
      MyGNBlock(...),
      MyGNBlock(...))
```

.footnote[[Relational inductive biases, deep learning, and graph networks](https://arxiv.org/abs/1806.01261)]

# Graph network API

### Update functions

```julia
update_edge(gn::T, e, vi, vj, u) where {T<:GraphNet} = e
update_vertex(gn::T, ē, vi, u) where {T<:GraphNet} = vi
update_global(gn::T, ē, v̄, u) where {T<:GraphNet} = u
```

### Aggregate functions

```julia
aggregate_neighbors(gn::T, aggr::Symbol, ...) where {T<:GraphNet}
aggregate_edges(gn::T, aggr::Symbol, ...) where {T<:GraphNet}
aggregate_vertices(gn::T, aggr::Symbol, ...) where {T<:GraphNet}
```

Feel free to override!

# Graph network is compatable with message passing scheme

```julia
abstract type MessagePassing <: GraphNet end
```

All behaviors are inherited from graph network and overrided by message-passing scheme.

# Variable graph input is supported

### What if I want to train my model with different graph structure?

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/fg.svg" width="60%">]

# FeaturedGraph goes through the whole network

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/fg2.svg" width="60%">]

* Support `GraphNetwork` and `MessagePassing`
* Support linear algebra related calculations
* Integrates well with other GeometricFlux layers

#### Graph-free layer can be used with FeaturedGraph input

```julia
GCNConv(num_features=>1000, relu)
```

# Working examples

* Graph convolution network (GCN)
* Graph autoencoder (GAE)

# Performance

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/gpu_scatter.svg" width="80%">]

Thank you, Tim!

.center[<img src="https://raw.githubusercontent.com/yuehhua/coscup2020/master/pics/cpu_scatter.svg" width="80%">]

Oops!

# Future work

* Support sparse array computation
  * on CPU and performance optimization
  * on cuda (CUDA/CUSPARSE)
* More layers/models
* Datasets inetegration

### Near future

* Example models:
  * Graph attention network (GAT)
  * Variational graph autoencoder (VGAE)
  * Neural graph differential equations (GDE)
* Documentation and tutorials
* Increase test cases
* Improve scatter performance on CPU
* Move to FluxML

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# Thank you for attention

### Pull request and issues are welcome.

slides: https://yuehhua.github.io/juliacon2020/geometricflux/

*This slides are generated by Remark.jl*