[COSCUP 2023] 我的Julia軟體架構演進之旅

我的 Julia 軟體架構演進之旅
Yueh-Hua Tu Ph.D.
ML Engineer@Taiwan AI Labs
Saturday 29th July, 2023
Y.H. Tu (Taiwan AI Labs) Evolutional Software Architecture Saturday 29th
July, 2023 1 / 28

Projects
Open source projects
FluxML/GeometricFlux.jl
yuehhua/GraphSignals.jl
yuehhua/CDGRNs.jl
JuliaSingleCellOmics/SnowyOwl.jl
Current work
Federated learning system architecture
Bioinformatics analysis tool development
(e.g. GWAS, linkage disequilibrium and
statistics)
Program optimization for execution
performance
July, 2023 2 / 28

Outline
1. Prototyping-Development Iteration
2. TDD and Test Coverage
3. Evolutionary Design
4. Principles of Software Engineering
5. Development Mindset
July, 2023 3 / 28

Developing from scratch
If we want a project for statistical analysis, where could we start?
July, 2023 4 / 28

Prototyping linear regression
Inference
y = Xβ
1 julia> predict(X, β)=X∗β
Fitting
β = (XT
X)−1
XT
y
1 julia> fit(X, y) = inv(X'*X) * X'*y
July, 2023 5 / 28

Prototyping-Development Iteration
July, 2023 6 / 28

Prototyping-Development Iteration (PDI)
Prototyping first then develop for flexibility and performance!
For prototyping and researching
Figure: Scripts
⇒
For development
Figure: Codebase
July, 2023 7 / 28

Prototyping
Figure: Scripts
Implement a simple and standalone feature in a script
to ensure correctness.
1 julia> predict(X, β)=X∗β
2
3 julia> fit(X, y) = inv(X'*X) * X'*y
And try to wrap these features into functions.
July, 2023 8 / 28

Development
Figure: Codebase
Puts functions or components into your codebase and design for
reusablility and flexibility.
1 struct LinearRegressionModel
2 β
3 end
4
5 coefs(m::LinearRegressionModel) = m.β
6
7 predict(m::LinearRegressionModel, X) = X*coefs(m)
8
9 fit(::Type{LinearRegressionModel}, X, y) =
LinearRegressionModel(inv(X'*X) * X'*y)
,
→
After putting components into codebase, ensure your scripts still work for you. Don't put to
much specifics into codebase (e.g. data, configs).
July, 2023 9 / 28

TDD and Test Coverage
July, 2023 10 / 28

Test-Driven Development (TDD)
Actually, PDI is a kind of TDD process.
We still need to add some test cases and ensure tests are passed.
July, 2023 11 / 28

TDD in Julia
TDD is very simple in Julia.
test/runtests.jl
1 @testset "MyProject.jl" begin
2 @test coefs(model) == β
3 ...
4 end
1 (@v1.9) pkg> test
July, 2023 12 / 28

Develop for flexibility
src/stats.jl
1 cov(X, Y) = innerprod(X, Y)
src/linalg.jl
1 innerprod(x, y) = x'*y
src/regression.jl
fit(::Type{LinearRegressionModel}, X, y) = LinearRegressionModel(inv(innerprod(X, X)) *
innerprod(X, y))
,
→
July, 2023 13 / 28

Develop for performance
src/linalg.jl
function innerprod(X::AbstractMatrix, y::AbstractVector)
# acceleration techniques: SIMD, GPU...
end
function innerprod(X::AbstractMatrix, Y::AbstractMatrix)
# acceleration techniques: SIMD, GPU...
end
Specification for optimization and keep consistent interfaces.
July, 2023 14 / 28

Test coverage
Keep high test coverage as possible to ensure your codebase provides desired features.
July, 2023 15 / 28

Evolutionary Design
Evolutionary Design
July, 2023 16 / 28

Evolutionary Design
Adding new features
If we want to add new approach to solve a linear system, how can we do?
Ax = b
1 using LinearSolve
2
3 A = rand(4, 4)
4 b = rand(4)
5 prob = LinearProblem(A, b)
6 sol = solve(prob)
Adding new features could be prototyped by
Adding types
Extending existing functions
July, 2023 17 / 28

Evolutionary Design
Adding types
Original
struct RegularSolver end
solve(m::RegularSolver, A, b) =
solve(LinearProblem(A, b))
,
→
⇒
Adding CholeskySolver
1 abstract type LinearSolver end
2
3 struct RegularSolver <: LinearSolver end
4 struct CholeskySolver <: LinearSolver end
5
6 solve(m::RegularSolver, A, b) =
,
→
July, 2023 18 / 28

Evolutionary Design
Extending functions/methods
Original
abstract type LinearSolver end
struct RegularSolver <:
LinearSolver end
,
→
struct CholeskySolver <:
LinearSolver end
,
→
solve(m::RegularSolver, A, b) =
,
→
⇒
Extending solve to CholeskySolver.
2
5
6 solve(m::RegularSolver, A, b) =
,
→
7 solve(m::CholeskySolver, A, b) = #
Cholesky decomposition...
,
→
July, 2023 19 / 28

Principles of Software Engineering
July, 2023 20 / 28

Single Responsibility Principle
A class or function should be responsible to only one actor.
LinearModel: represent a linear model
coefs: returns model coeﬀicients
fit: fitting model
LinearSolver: approach to solve a linear system
solve: determines how to solve a linear system
July, 2023 21 / 28

Open-Closed Principle
A module will be said to be open for extension and be said to be closed for modification.
struct LinearRegressionModel
β
end
coefs(m::LinearRegressionModel) =
m.β
⇒
1 abstract type LinearModel end
2
3 struct LinearRegressionModel <:
LinearModel
,
→
4 β
5 end
6
7 struct LogisticRegressionModel <:
LinearModel
,
→
8 β
9 end
10
12 coefs(m::LogisticRegressionModel) = m.β
Benefitial from Julia's features of type and method separation, it's easy to add new types or
July, 2023 22 / 28

Liskov Substitution Principle
Derived class can be able to replace its base class.
abstract type LinearModel end
struct LinearRegressionModel <: LinearModel
β
end
struct LogisticRegressionModel <: LinearModel
β
end
Since Julia's subtyping system doesn't allow composite types as super types, Julia always run
composite types in practice.
July, 2023 23 / 28

Interface Segregation Principle
No code should be forced to depend on methods it does not use.
1 julia> model = fit(LinearRegressionModel,
X, y)
,
→
2
3 julia> coefs(model)
4
5 julia> model =
fit(LogisticRegressionModel, X, y)
,
→
6
7 julia> coefs(model)
User just need to know interfaces.
1 module MyPackage
LinearModel
,
→
4 β
5 end
LinearModel
,
→
7 β
8 end
10 coefs(m::LogisticRegressionModel) =
m.β
11 end
July, 2023 24 / 28

Dependency Inversion Principle
Dependency injection
2
5 struct QRSolver <: LinearSolver end
2
LinearModel end
,
→
LinearModel end
,
→
5
6 fit(::Type{LinearRegressionModel}, X, y;
algo=CholeskySolver())
,
→
7 fit(::Type{LogisticRegressionModel}, X, y;
algo=CholeskySolver())
,
→
July, 2023 25 / 28

Development Mindset
Development Mindset
July, 2023 26 / 28

Development Mindset
Issues and Pull Requests
貢獻者
遇到善於給建議（機車）的人
如何主持跟維護專案
July, 2023 27 / 28

Development Mindset
Thank you for attention
July, 2023 28 / 28

[COSCUP 2023] 我的Julia軟體架構演進之旅

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