Implements a simple Nutpie style adaptation (using both positions and gradients, but not changing the schedule).

HISTORY.md

@@ -1,5 +1,12 @@
 # AdvancedHMC Changelog
 
+## 0.8.4
+
+  - Introduces an experimental way to improve the *diagonal* mass matrix adaptation using gradient information (similar to [nutpie](https://github.com/pymc-devs/nutpie)),
+      currently to be initialized for a `metric` of type `DiagEuclideanMetric`
+      via `mma = AdvancedHMC.NutpieVar(size(metric); var=copy(metric.M⁻¹))`
+      until a new interface is introduced in an upcoming breaking release to specify the method of adaptation.
+
 ## 0.8.0
 
   - To make an MCMC transtion from phasepoint `z` using trajectory `τ`(or HMCKernel `κ`) under Hamiltonian `h`, use `transition(h, τ, z)` or `transition(rng, h, τ, z)`(if using HMCKernel, use `transition(h, κ, z)` or `transition(rng, h, κ, z)`).

Project.toml

@@ -1,6 +1,6 @@
 name = "AdvancedHMC"
 uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d"
-version = "0.8.3"
+version = "0.8.4"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"

docs/src/api.md

@@ -31,11 +31,15 @@ where `ϵ` is the step size of leapfrog integration.
 ### Adaptor (`adaptor`)
 
   - Adapt the mass matrix `metric` of the Hamiltonian dynamics: `mma = MassMatrixAdaptor(metric)`
-    
+
       + This is lowered to `UnitMassMatrix`, `WelfordVar` or `WelfordCov` based on the type of the mass matrix `metric`
+      + There is an experimental way to improve the *diagonal* mass matrix adaptation using gradient information (similar to [nutpie](https://github.com/pymc-devs/nutpie)),
+      currently to be initialized for a `metric` of type `DiagEuclideanMetric`
+      via `mma = AdvancedHMC.NutpieVar(size(metric); var=copy(metric.M⁻¹))`
+      until a new interface is introduced in an upcoming breaking release to specify the method of adaptation.
 
   - Adapt the step size of the leapfrog integrator `integrator`: `ssa = StepSizeAdaptor(δ, integrator)`
-    
+
       + It uses Nesterov's dual averaging with `δ` as the target acceptance rate.
   - Combine the two above *naively*: `NaiveHMCAdaptor(mma, ssa)`
   - Combine the first two using Stan's windowed adaptation: `StanHMCAdaptor(mma, ssa)`
@@ -60,12 +64,12 @@ sample(
 Draw `n_samples` samples using the kernel `κ` under the Hamiltonian system `h`
 
   - The randomness is controlled by `rng`.
-    
+
       + If `rng` is not provided, the default random number generator (`Random.default_rng()`) will be used.
 
   - The initial point is given by `θ`.
   - The adaptor is set by `adaptor`, for which the default is no adaptation.
-    
+
       + It will perform `n_adapts` steps of adaptation, for which the default is `1_000` or 10% of `n_samples`, whichever is lower.
   - `drop_warmup` specifies whether to drop samples.
   - `verbose` controls the verbosity.

src/AdvancedHMC.jl

@@ -72,7 +72,7 @@ export find_good_eps
 include("adaptation/Adaptation.jl")
 using .Adaptation
 import .Adaptation:
-    StepSizeAdaptor, MassMatrixAdaptor, StanHMCAdaptor, NesterovDualAveraging, NoAdaptation
+    StepSizeAdaptor, MassMatrixAdaptor, StanHMCAdaptor, NesterovDualAveraging, NoAdaptation, PositionOrPhasePoint
 
 # Helpers for initializing adaptors via AHMC structs
 
@@ -114,6 +114,7 @@ export StepSizeAdaptor,
     MassMatrixAdaptor,
     UnitMassMatrix,
     WelfordVar,
+    NutpieVar,
     WelfordCov,
     NaiveHMCAdaptor,
     StanHMCAdaptor,

src/abstractmcmc.jl

@@ -196,7 +196,7 @@ function AbstractMCMC.step(
 
     # Adapt h and spl.
     tstat = stat(t)
-    h, κ, isadapted = adapt!(h, κ, adaptor, i, n_adapts, t.z.θ, tstat.acceptance_rate)
+    h, κ, isadapted = adapt!(h, κ, adaptor, i, n_adapts, t.z, tstat.acceptance_rate)
     tstat = merge(tstat, (is_adapt=isadapted,))
 
     # Compute next transition and state.

src/adaptation/Adaptation.jl

@@ -4,13 +4,13 @@ export Adaptation
 using LinearAlgebra: LinearAlgebra
 using Statistics: Statistics
 
-using ..AdvancedHMC: AbstractScalarOrVec
+using ..AdvancedHMC: AbstractScalarOrVec, PhasePoint
 using DocStringExtensions
 
 """
 $(TYPEDEF)
 
-Abstract type for HMC adaptors. 
+Abstract type for HMC adaptors.
 """
 abstract type AbstractAdaptor end
 function getM⁻¹ end
@@ -21,12 +21,17 @@ function initialize! end
 function finalize! end
 export AbstractAdaptor, adapt!, initialize!, finalize!, reset!, getϵ, getM⁻¹
 
+get_position(x::PhasePoint) = x.θ
+get_position(x::AbstractVecOrMat{<:AbstractFloat}) = x
+const PositionOrPhasePoint = Union{AbstractVecOrMat{<:AbstractFloat}, PhasePoint}
+
 struct NoAdaptation <: AbstractAdaptor end
 export NoAdaptation
 include("stepsize.jl")
 export StepSizeAdaptor, NesterovDualAveraging
+
 include("massmatrix.jl")
-export MassMatrixAdaptor, UnitMassMatrix, WelfordVar, WelfordCov
+export MassMatrixAdaptor, UnitMassMatrix, WelfordVar, NutpieVar, WelfordCov
 
 ##
 ## Composite adaptors
@@ -47,18 +52,14 @@ getϵ(ca::NaiveHMCAdaptor) = getϵ(ca.ssa)
 # TODO: implement consensus adaptor
 function adapt!(
     nca::NaiveHMCAdaptor,
-    θ::AbstractVecOrMat{<:AbstractFloat},
+    z_or_theta::PositionOrPhasePoint,
     α::AbstractScalarOrVec{<:AbstractFloat},
 )
-    adapt!(nca.ssa, θ, α)
-    adapt!(nca.pc, θ, α)
-    return nothing
-end
-function reset!(aca::NaiveHMCAdaptor)
-    reset!(aca.ssa)
-    reset!(aca.pc)
+    adapt!(nca.ssa, z_or_theta, α)
+    adapt!(nca.pc, z_or_theta, α)
     return nothing
 end
+
 initialize!(adaptor::NaiveHMCAdaptor, n_adapts::Int) = nothing
 finalize!(aca::NaiveHMCAdaptor) = finalize!(aca.ssa)
 

src/adaptation/massmatrix.jl

@@ -9,16 +9,18 @@ finalize!(::MassMatrixAdaptor) = nothing
 
 function adapt!(
     adaptor::MassMatrixAdaptor,
-    θ::AbstractVecOrMat{<:AbstractFloat},
-    α::AbstractScalarOrVec{<:AbstractFloat},
+    z_or_theta::PositionOrPhasePoint,
+    ::AbstractScalarOrVec{<:AbstractFloat},
     is_update::Bool=true,
 )
-    resize_adaptor!(adaptor, size(θ))
-    push!(adaptor, θ)
+    resize_adaptor!(adaptor, size(get_position(z_or_theta)))
+    push!(adaptor, z_or_theta)
     is_update && update!(adaptor)
     return nothing
 end
 
+Base.push!(a::MassMatrixAdaptor, z_or_theta::PositionOrPhasePoint) = push!(a, get_position(z_or_theta))
+
 ## Unit mass matrix adaptor
 
 struct UnitMassMatrix{T<:AbstractFloat} <: MassMatrixAdaptor end
@@ -39,15 +41,14 @@ getM⁻¹(::UnitMassMatrix{T}) where {T} = LinearAlgebra.UniformScaling{T}(one(T
 
 function adapt!(
     ::UnitMassMatrix,
-    ::AbstractVecOrMat{<:AbstractFloat},
+    ::PositionOrPhasePoint,
     ::AbstractScalarOrVec{<:AbstractFloat},
     is_update::Bool=true,
 )
     return nothing
 end
 
 ## Diagonal mass matrix adaptor
-
 abstract type DiagMatrixEstimator{T} <: MassMatrixAdaptor end
 
 getM⁻¹(ve::DiagMatrixEstimator) = ve.var
@@ -70,7 +71,7 @@ NaiveVar{T}(sz::Tuple{Int,Int}) where {T<:AbstractFloat} = NaiveVar(Vector{Matri
 
 NaiveVar(sz::Union{Tuple{Int},Tuple{Int,Int}}) = NaiveVar{Float64}(sz)
 
-Base.push!(nv::NaiveVar, s::AbstractVecOrMat) = push!(nv.S, s)
+Base.push!(nv::NaiveVar, s::AbstractVecOrMat{<:AbstractFloat}) = push!(nv.S, s)
 
 reset!(nv::NaiveVar) = resize!(nv.S, 0)
 
@@ -135,7 +136,7 @@ function reset!(wv::WelfordVar{T}) where {T<:AbstractFloat}
     return nothing
 end
 
-function Base.push!(wv::WelfordVar, s::AbstractVecOrMat{T}) where {T}
+function Base.push!(wv::WelfordVar, s::AbstractVecOrMat{T}) where {T<:AbstractFloat}
     wv.n += 1
     (; δ, μ, M, n) = wv
     n = T(n)
@@ -153,6 +154,90 @@ function get_estimation(wv::WelfordVar{T}) where {T<:AbstractFloat}
     return n / ((n + 5) * (n - 1)) * M .+ ϵ * (5 / (n + 5))
 end
 
+"""
+    NutpieVar
+
+Nutpie-style diagonal mass matrix estimator (using positions and gradients).
+
+Expected to converge faster and to a better mass matrix than [`WelfordVar`](@ref), for which it is a drop-in replacement.
+
+Can be initialized via `NutpieVar(sz)` where `sz` is either a `Tuple{Int}` or a `Tuple{Int,Int}`.
+
+# Fields
+
+$(FIELDS)
+"""
+mutable struct NutpieVar{T<:AbstractFloat,E<:AbstractVecOrMat{T},V<:AbstractVecOrMat{T}} <: DiagMatrixEstimator{T}
+    "Online variance estimator of the posterior positions."
+    position_estimator::WelfordVar{T,E,V}
+    "Online variance estimator of the posterior gradients."
+    gradient_estimator::WelfordVar{T,E,V}
+    "The number of observations collected so far."
+    n::Int
+    "The minimal number of observations after which the estimate of the variances can be updated."
+    n_min::Int
+    "The estimated variances - initialized to ones, updated after calling [`update!`](@ref) if `n > n_min`."
+    var::V
+    function NutpieVar(n::Int, n_min::Int, μ::E, M::E, δ::E, var::V) where {E,V}
+        return new{eltype(E),E,V}(
+            WelfordVar(n, n_min, copy(μ), copy(M), copy(δ), copy(var)),
+            WelfordVar(n, n_min, copy(μ), copy(M), copy(δ), copy(var)),
+            n, n_min, var
+        )
+    end
+end
+
+function Base.show(io::IO, ::NutpieVar{T}) where {T}
+    return print(io, "NutpieVar{", T, "} adaptor")
+end
+
+function NutpieVar{T}(
+    sz::Union{Tuple{Int},Tuple{Int,Int}}=(2,); n_min::Int=10, var=ones(T, sz)
+) where {T<:AbstractFloat}
+    return NutpieVar(0, n_min, zeros(T, sz), zeros(T, sz), zeros(T, sz), var)
+end
+
+function NutpieVar(sz::Union{Tuple{Int},Tuple{Int,Int}}; kwargs...)
+    return NutpieVar{Float64}(sz; kwargs...)
+end
+
+function resize_adaptor!(nv::NutpieVar{T}, size_θ::Tuple{Int,Int}) where {T<:AbstractFloat}
+    if size_θ != size(nv.var)
+        @assert nv.n == 0 "Cannot resize a var estimator when it contains samples."
+        resize_adaptor!(nv.position_estimator, size_θ)
+        resize_adaptor!(nv.gradient_estimator, size_θ)
+        nv.var = ones(T, size_θ)
+    end
+end
+
+function resize_adaptor!(nv::NutpieVar{T}, size_θ::Tuple{Int}) where {T<:AbstractFloat}
+    length_θ = first(size_θ)
+    if length_θ != size(nv.var, 1)
+        @assert nv.n == 0 "Cannot resize a var estimator when it contains samples."
+        resize_adaptor!(nv.position_estimator, size_θ)
+        resize_adaptor!(nv.gradient_estimator, size_θ)
+        fill!(resize!(nv.var, length_θ), T(1))
+    end
+end
+
+function reset!(nv::NutpieVar)
+    nv.n = 0
+    reset!(nv.position_estimator)
+    reset!(nv.gradient_estimator)
+end
+
+Base.push!(::NutpieVar, x::AbstractVecOrMat{<:AbstractFloat}) = error("`NutpieVar` adaptation requires position and gradient information!")
+
+function Base.push!(nv::NutpieVar, z::PhasePoint)
+    nv.n += 1
+    push!(nv.position_estimator, z.θ)
+    push!(nv.gradient_estimator, z.ℓπ.gradient)
+    return nothing
+end
+
+# Ref: https://github.com/pymc-devs/nutpie
+get_estimation(nv::NutpieVar) = sqrt.(get_estimation(nv.position_estimator) ./ get_estimation(nv.gradient_estimator))
+
 ## Dense mass matrix adaptor
 
 abstract type DenseMatrixEstimator{T} <: MassMatrixAdaptor end
@@ -175,7 +260,7 @@ end
 
 NaiveCov{T}(sz::Tuple{Int}) where {T<:AbstractFloat} = NaiveCov(Vector{Vector{T}}())
 
-Base.push!(nc::NaiveCov, s::AbstractVector) = push!(nc.S, s)
+Base.push!(nc::NaiveCov, s::AbstractVector{<:AbstractFloat}) = push!(nc.S, s)
 
 reset!(nc::NaiveCov{T}) where {T} = resize!(nc.S, 0)
 
@@ -225,7 +310,7 @@ function reset!(wc::WelfordCov{T}) where {T<:AbstractFloat}
     return nothing
 end
 
-function Base.push!(wc::WelfordCov, s::AbstractVector{T}) where {T}
+function Base.push!(wc::WelfordCov, s::AbstractVector{T}) where {T<:AbstractFloat}
     wc.n += 1
     (; δ, μ, n, M) = wc
     n = T(n)

src/adaptation/stan_adaptor.jl

@@ -136,20 +136,20 @@ is_window_end(a::StanHMCAdaptor) = a.state.i in a.state.window_splits
 
 function adapt!(
     tp::StanHMCAdaptor,
-    θ::AbstractVecOrMat{<:AbstractFloat},
+    z_or_theta::PositionOrPhasePoint,
     α::AbstractScalarOrVec{<:AbstractFloat},
 )
     tp.state.i += 1
 
-    adapt!(tp.ssa, θ, α)
+    adapt!(tp.ssa, z_or_theta, α)
 
-    resize_adaptor!(tp.pc, size(θ)) # Resize pre-conditioner if necessary.
+    resize_adaptor!(tp.pc, size(get_position(z_or_theta))) # Resize pre-conditioner if necessary.
 
     # Ref: https://github.com/stan-dev/stan/blob/develop/src/stan/mcmc/hmc/nuts/adapt_diag_e_nuts.hpp
     if is_in_window(tp)
         # We accumlate stats from θ online and only trigger the update of M⁻¹ in the end of window.
         is_update_M⁻¹ = is_window_end(tp)
-        adapt!(tp.pc, θ, α, is_update_M⁻¹)
+        adapt!(tp.pc, z_or_theta, α, is_update_M⁻¹)
     end
 
     if is_window_end(tp)

src/adaptation/stepsize.jl

@@ -174,7 +174,7 @@ end
 # Ref: https://github.com/stan-dev/stan/blob/develop/src/stan/mcmc/stepsize_adaptation.hpp
 # Note: This function is not merged with `adapt!` to empahsize the fact that
 #       step size adaptation is not dependent on `θ`.
-# Note 2: `da.state` and `α` support vectorised HMC but should do so together. 
+# Note 2: `da.state` and `α` support vectorised HMC but should do so together.
 function adapt_stepsize!(
     da::NesterovDualAveraging{T}, α::AbstractScalarOrVec{T}
 ) where {T<:AbstractFloat}
@@ -211,7 +211,7 @@ end
 
 function adapt!(
     da::NesterovDualAveraging,
-    θ::AbstractVecOrMat{<:AbstractFloat},
+    ::PositionOrPhasePoint,
     α::AbstractScalarOrVec{<:AbstractFloat},
 )
     adapt_stepsize!(da, α)