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DenseMetric and Component arrays (solve #344) #345

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DenseMetric and Component arrays (solve #344) #345

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dbb5d16
correct length check for phase point
erathorn Aug 3, 2023
18dc7f0
fix typo
erathorn Aug 3, 2023
449a589
DenseMetric and Comoponent Arrays
erathorn Aug 3, 2023
7c58524
Update test/hamiltonian.jl
erathorn Aug 3, 2023
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9 changes: 6 additions & 3 deletions src/hamiltonian.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -42,16 +42,19 @@ end
∂H∂r(h::Hamiltonian{<:UnitEuclideanMetric,<:GaussianKinetic}, r::AbstractVecOrMat) = copy(r)
∂H∂r(h::Hamiltonian{<:DiagEuclideanMetric,<:GaussianKinetic}, r::AbstractVecOrMat) =
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h.metric.M⁻¹ .* r
∂H∂r(h::Hamiltonian{<:DenseEuclideanMetric,<:GaussianKinetic}, r::AbstractVecOrMat) =
h.metric.M⁻¹ * r
function ∂H∂r(h::Hamiltonian{<:DenseEuclideanMetric,<:GaussianKinetic}, r::AbstractVecOrMat)
out = similar(r) # Make sure the output of this function is of the same type as r
mul!(out, h.metric.M⁻¹, r)
out
end
Comment on lines +45 to +49
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I'm a bit uncertain about this change as it "complicates" code to mainly just stay compatible with ComponentArrays.jl, and thus I'd be more in favour of just making it an extension instead, I think 😕 Then in the extension, we just overload whatever we need to be compatible.

Also, will this code break if, say, h.metric.M⁻¹ has eltype Float64 but r has eltype Float32, rather than just promoting, as is current behavior?

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p1 = ComponentArray(m=one(Float32), s = one(Float32))
r = similar(p1)
M = diagm(randn(Float64, 2))
mul!(r, M, p1)

This works on my machine, and returns r as a component array of eltype Float32 as expected.

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AHMC supports vectorised sampling, when passing arguments in a suitable type. In this case, r::AbstractVecOrMat could be a single momentum realization or a vector of momentum realizations. Therefore, the new code needs to be able to handle the vectorized sampling mode for the tests to pass.

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Sorry for the silence. Thank you for the suggestion, it totally makes sense to me. However, I looked into this a bit more and am honestly slightly lost. The call to the rand function, which fails in the tests only works in the test case. Calling this function in a plain Julia session fails for me (on the main branch). A brute force solution, which dispatches on r::AbtractVecOrMat{AbstractVecOrMat}, does unfortunately not do the trick either.


struct PhasePoint{T<:AbstractVecOrMat{<:AbstractFloat},V<:DualValue}
θ::T # Position variables / model parameters.
r::T # Momentum variables
ℓπ::V # Cached neg potential energy for the current θ.
ℓκ::V # Cached neg kinect energy for the current r.
function PhasePoint(θ::T, r::T, ℓπ::V, ℓκ::V) where {T,V}
@argcheck length(θ) == length(r) == length(ℓπ.gradient) == length(ℓπ.gradient)
@argcheck length(θ) == length(r) == length(ℓπ.gradient) == length(ℓκ.gradient)
if any(isfinite.((θ, r, ℓπ, ℓκ)) .== false)
# @warn "The current proposal will be rejected due to numerical error(s)." isfinite.((θ, r, ℓπ, ℓκ))
# NOTE eltype has to be inlined to avoid type stability issue; see #267
Expand Down
35 changes: 34 additions & 1 deletion test/hamiltonian.jl
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
using ReTest, AdvancedHMC
using AdvancedHMC: GaussianKinetic, DualValue, PhasePoint
using LinearAlgebra: dot, diagm
using ComponentArrays

@testset "Hamiltonian" begin
f = x -> dot(x, x)
Expand Down Expand Up @@ -38,7 +39,7 @@
end
end

@testset "Metric" begin
@testset "Metric Base Array" begin
n_tests = 10

for T in [Float32, Float64]
Expand All @@ -49,18 +50,50 @@
h = Hamiltonian(UnitEuclideanMetric(T, D), ℓπ, ∂ℓπ∂θ)
@test -AdvancedHMC.neg_energy(h, r_init, θ_init) == sum(abs2, r_init) / 2
@test AdvancedHMC.∂H∂r(h, r_init) == r_init
@test typeof(AdvancedHMC.∂H∂r(h, r_init)) == typeof(r_init)

M⁻¹ = ones(T, D) + abs.(randn(T, D))
h = Hamiltonian(DiagEuclideanMetric(M⁻¹), ℓπ, ∂ℓπ∂θ)
@test -AdvancedHMC.neg_energy(h, r_init, θ_init) ≈
r_init' * diagm(0 => M⁻¹) * r_init / 2
@test AdvancedHMC.∂H∂r(h, r_init) == M⁻¹ .* r_init
@test typeof(AdvancedHMC.∂H∂r(h, r_init)) == typeof(r_init)

m = randn(T, D, D)
M⁻¹ = m' * m
h = Hamiltonian(DenseEuclideanMetric(M⁻¹), ℓπ, ∂ℓπ∂θ)
@test -AdvancedHMC.neg_energy(h, r_init, θ_init) ≈ r_init' * M⁻¹ * r_init / 2
@test AdvancedHMC.∂H∂r(h, r_init) == M⁻¹ * r_init
@test typeof(AdvancedHMC.∂H∂r(h, r_init)) == typeof(r_init)
end
end
end

@testset "Metric ComponentArrays" begin
n_tests = 10
for T in [Float32, Float64]
for _ = 1:n_tests
θ_init = ComponentArray(a = randn(T, D), b = randn(T, D))
r_init = ComponentArray(a = randn(T, D), b = randn(T, D))

h = Hamiltonian(UnitEuclideanMetric(T, 2*D), ℓπ, ∂ℓπ∂θ)

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Raw Output:
test/hamiltonian.jl:79:-            h = Hamiltonian(UnitEuclideanMetric(T, 2*D), ℓπ, ∂ℓπ∂θ)
test/hamiltonian.jl:79:+            h = Hamiltonian(UnitEuclideanMetric(T, 2 * D), ℓπ, ∂ℓπ∂θ)
@test -AdvancedHMC.neg_energy(h, r_init, θ_init) == sum(abs2, r_init) / 2
@test AdvancedHMC.∂H∂r(h, r_init) == r_init
@test typeof(AdvancedHMC.∂H∂r(h, r_init)) == typeof(r_init)

M⁻¹ = ones(T, 2*D) + abs.(randn(T, 2*D))

Check warning on line 84 in test/hamiltonian.jl

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Raw Output:
test/hamiltonian.jl:84:-            M⁻¹ = ones(T, 2*D) + abs.(randn(T, 2*D))
test/hamiltonian.jl:84:+            M⁻¹ = ones(T, 2 * D) + abs.(randn(T, 2 * D))
h = Hamiltonian(DiagEuclideanMetric(M⁻¹), ℓπ, ∂ℓπ∂θ)
@test -AdvancedHMC.neg_energy(h, r_init, θ_init) ≈
r_init' * diagm(0 => M⁻¹) * r_init / 2
@test AdvancedHMC.∂H∂r(h, r_init) == M⁻¹ .* r_init
@test typeof(AdvancedHMC.∂H∂r(h, r_init)) == typeof(r_init)

m = randn(T, 2*D, 2*D)

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Raw Output:
test/hamiltonian.jl:91:-            m = randn(T, 2*D, 2*D)
test/hamiltonian.jl:91:+            m = randn(T, 2 * D, 2 * D)
M⁻¹ = m' * m
h = Hamiltonian(DenseEuclideanMetric(M⁻¹), ℓπ, ∂ℓπ∂θ)
@test -AdvancedHMC.neg_energy(h, r_init, θ_init) ≈ r_init' * M⁻¹ * r_init / 2
@test all(AdvancedHMC.∂H∂r(h, r_init) .== M⁻¹ * r_init)
@test typeof(AdvancedHMC.∂H∂r(h, r_init)) == typeof(r_init)
end
end
end
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