朱莉娅（Julia）中具有容器类型和矩阵向量乘积的类型稳定性

我正在尝试将Julia'sA_mul_B!与容器类型配合使用，例如

# my composite type, contains 2 vectors and 1 matrix of same Float type
type MyContainer{T <: Float}
    z :: Vector
    x :: Matrix
    y :: Vector
    MyContainer(z::Vector{T}, x::Matrix{T}, y::Vector{T}) = new(z,x,y) 
end

然后，我使用MyContainerwith的实例，A_mul_B!然后对Vector对象进行一些算术运算：

# only work with single/double precision
typealias Float Union{Float32, Float64}

# function to perform mat-vec multiply
function f{T <: Float}(v::MyContainer{T}) 
    Base.A_mul_B!(v.z, v.x, v.y)
    return sumabs2(v.z) * sumabs2(v.y)
end

正如定义的f那样，即使构造函数本身是类型稳定的，它也不是类型稳定的。是否有一个地方我可以注释类型的地方z，x以及y让A_mul_B!看到他们？

这是一个最小的工作示例：

MyModule.jl

module MyModule

export MyContainer, f

# only work with single/double precision
typealias Float Union{Float32, Float64}

# my composite type, contains 2 vectors and 1 matrix of same Float type
type MyContainer{T <: Float}
    z :: Vector
    x :: Matrix
    y :: Vector
    MyContainer(z::Vector{T}, x::Matrix{T}, y::Vector{T}) = new(z,x,y) 
end

# testing routine initializes all arrays with a single value 
function MyContainer{T <: Float}(n::Int, t::T)
    z = t*ones(T, n)
    x = t*ones(T, (n,n))
    y = t*ones(T, n)
    return MyContainer{eltype(z)}(z, x, y)
end

# function to perform mat-vec multiply
function f{T <: Float}(v::MyContainer{T}) 
    Base.A_mul_B!(v.z, v.x, v.y)
    return sumabs2(v.z) * sumabs2(v.y)
end

end

test.jl

include("MyModule.jl")

function g() 
    # check type stability
    @code_warntype MyModule.MyContainer(10, 1.0) # type-stable
    @code_warntype MyModule.f(v) # red Array{T,1}, Array{T,2}, Any

    # make a container
    v = MyModule.MyContainer(10, 1.0)

    # does type-stability matter for performance?
    @time 1+1 
    MyModule.f(v)
    @time MyModule.f(v) # maybe... note small memory allocation
end

g()

部分输出

# omit output of @code_warntype for conciseness
  0.000000 seconds
  0.000001 seconds (3 allocations: 48 bytes)
10000.0