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Merged
prbzrg merged 1 commit into from
May 14, 2026
Merged

switch to Float64 #525

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3 changes: 1 addition & 2 deletions benchmark/benchmarks.jl
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Expand Up @@ -10,9 +10,8 @@ import ADTypes,

ndata = 2^10
ndimensions = 1
data_dist = Distributions.Beta{Float32}(2.0f0, 4.0f0)
data_dist = Distributions.Beta(2.0, 4.0)
r = rand(data_dist, ndimensions, ndata)
r = convert.(Float32, r)

nvariables = size(r, 1)
icnf = ContinuousNormalizingFlows.ICNF(; nvariables)
Expand Down
27 changes: 13 additions & 14 deletions examples/usage.jl
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Expand Up @@ -6,9 +6,8 @@ global_logger(TerminalLogger())
using Distributions
ndata = 1024
ndimensions = 1
data_dist = Beta{Float32}(2.0f0, 4.0f0)
data_dist = Beta(2.0, 4.0)
r = rand(data_dist, ndimensions, ndata)
r = convert.(Float32, r)

## Parameters
nvariables = size(r, 1)
Expand Down Expand Up @@ -40,11 +39,11 @@ icnf = ICNF(;
nvariables = nvariables, # number of variables
naugments = naugments, # number of augmented dimensions
nconditions = 0, # number of conditioning inputs
λ₁ = 1.0f-2, # regulate flow
λ₂ = 1.0f-2, # regulate volume change
λ₃ = 1.0f-2, # regulate augmented dimensions
steer_rate = 1.0f-1, # add random noise to end of the time span
tspan = (0.0f0, 1.0f0), # time span
λ₁ = 0.01, # regulate flow
λ₂ = 0.01, # regulate volume change
λ₃ = 0.01, # regulate augmented dimensions
steer_rate = 0.1, # add random noise to end of the time span
tspan = (0.0, 1.0), # time span
device = cpu_device(), # process data by CPU
# device = gpu_device(), # process data by GPU
autonomous = false, # using non-autonomous flow
Expand All @@ -54,8 +53,8 @@ icnf = ICNF(;
sol_kwargs = (;
save_everystep = false,
maxiters = typemax(Int),
reltol = 1.0f-4,
abstol = 1.0f-4,
reltol = 1.0e-4,
abstol = 1.0e-4,
alg = VCABM(; thread = Threaded()),
sensealg = InterpolatingAdjoint(;
checkpointing = true,
Expand Down Expand Up @@ -84,9 +83,9 @@ if !isfile(icnf_mach_fn)
icnf,
optimizers = (
OptimiserChain(
WeightDecay(; lambda = 1.0f-4),
ClipNorm(1.0f0, 2.0f0; throw = true),
Adam(; eta = 1.0f-3, beta = (9.0f-1, 9.99f-1), epsilon = 1.0f-8),
WeightDecay(; lambda = 1.0e-4),
ClipNorm(1.0, 2.0; throw = true),
Adam(; eta = 0.001, beta = (0.9, 0.999), epsilon = 1.0e-8),
),
),
batchsize = 1024,
Expand Down Expand Up @@ -124,8 +123,8 @@ display(res_df)
using CairoMakie
f = Figure()
ax = Axis(f[1, 1]; title = "Result")
lines!(ax, 0.0f0 .. 1.0f0, x -> pdf(data_dist, x); label = "Actual")
lines!(ax, 0.0f0 .. 1.0f0, x -> pdf(d, vcat(x)); label = "Estimated")
lines!(ax, 0.0 .. 1.0, x -> pdf(data_dist, x); label = "Actual")
lines!(ax, 0.0 .. 1.0, x -> pdf(d, vcat(x)); label = "Estimated")
axislegend(ax)
save("result-figure.svg", f)
save("result-figure.png", f)
10 changes: 5 additions & 5 deletions src/core/icnf.jl
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Expand Up @@ -51,7 +51,7 @@ struct ICNF{
end

function ICNF(;
data_type::Type{<:AbstractFloat} = Float32,
data_type::Type{<:AbstractFloat} = Float64,
compute_mode::ComputeMode = LuxVecJacMatrixMode(ADTypes.AutoZygote()),
inplace::Bool = false,
autonomous::Bool = false,
Expand All @@ -69,10 +69,10 @@ function ICNF(;
Lux.Dense(n_hidden => n_hidden, NNlib.softplus),
Lux.Dense(n_hidden => n_out),
),
steer_rate::AbstractFloat = convert(data_type, 1.0e-1),
λ₁::AbstractFloat = convert(data_type, 1.0e-2),
λ₂::AbstractFloat = convert(data_type, 1.0e-2),
λ₃::AbstractFloat = convert(data_type, 1.0e-2),
steer_rate::AbstractFloat = convert(data_type, 0.1),
λ₁::AbstractFloat = convert(data_type, 0.01),
λ₂::AbstractFloat = convert(data_type, 0.01),
λ₃::AbstractFloat = convert(data_type, 0.01),
basedist::Distributions.Distribution = Distributions.MvNormal(
FillArrays.Zeros{data_type}(nvariables + naugments),
FillArrays.Eye{data_type}(nvariables + naugments),
Expand Down
6 changes: 3 additions & 3 deletions src/exts/mlj_ext/core_cond_icnf.jl
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Expand Up @@ -15,12 +15,12 @@ function CondICNFModel(;
Optimisers.WeightDecay(; lambda = convert(eltype(icnf), 1.0e-4)),
Optimisers.ClipNorm(
one(eltype(icnf)),
convert(eltype(icnf), 2.0e0);
convert(eltype(icnf), 2.0);
throw = true,
),
Optimisers.Adam(;
eta = convert(eltype(icnf), 1.0e-3),
beta = (convert(eltype(icnf), 9.0e-1), convert(eltype(icnf), 9.99e-1)),
eta = convert(eltype(icnf), 0.001),
beta = (convert(eltype(icnf), 0.9), convert(eltype(icnf), 0.999)),
epsilon = convert(eltype(icnf), 1.0e-8),
),
),
Expand Down
6 changes: 3 additions & 3 deletions src/exts/mlj_ext/core_icnf.jl
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Expand Up @@ -15,12 +15,12 @@ function ICNFModel(;
Optimisers.WeightDecay(; lambda = convert(eltype(icnf), 1.0e-4)),
Optimisers.ClipNorm(
one(eltype(icnf)),
convert(eltype(icnf), 2.0e0);
convert(eltype(icnf), 2.0);
throw = true,
),
Optimisers.Adam(;
eta = convert(eltype(icnf), 1.0e-3),
beta = (convert(eltype(icnf), 9.0e-1), convert(eltype(icnf), 9.99e-1)),
eta = convert(eltype(icnf), 0.001),
beta = (convert(eltype(icnf), 0.9), convert(eltype(icnf), 0.999)),
epsilon = convert(eltype(icnf), 1.0e-8),
),
),
Expand Down
2 changes: 1 addition & 1 deletion src/layers/planar_layer.jl
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Expand Up @@ -15,7 +15,7 @@ function PlanarLayer(
mapping::Pair{<:Int, <:Int},
activation::Any = identity;
init_weight::Any = WeightInitializers.glorot_uniform,
init_bias::Any = WeightInitializers.zeros32,
init_bias::Any = WeightInitializers.zeros64,
use_bias::Bool = true,
)
return PlanarLayer{
Expand Down
3 changes: 1 addition & 2 deletions test/ci_tests/regression_tests.jl
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@@ -1,9 +1,8 @@
Test.@testset verbose = true showtiming = true failfast = false "Regression Tests" begin
ndata = 2^10
ndimensions = 1
data_dist = Distributions.Beta{Float32}(2.0f0, 4.0f0)
data_dist = Distributions.Beta(2.0, 4.0)
r = rand(data_dist, ndimensions, ndata)
r = convert.(Float32, r)

nvariables = size(r, 1)
icnf = ContinuousNormalizingFlows.ICNF(; nvariables)
Expand Down
13 changes: 6 additions & 7 deletions test/ci_tests/smoke_tests.jl
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Expand Up @@ -9,14 +9,14 @@ Test.@testset verbose = true showtiming = true failfast = false "Smoke Tests" be

ndata = 4
ndimensions = 2
data_dist = Distributions.Beta{Float32}(2.0f0, 4.0f0)
data_dist2 = Distributions.Beta{Float32}(2.0f0, 4.0f0)
data_dist = Distributions.Beta(2.0, 4.0)
data_dist2 = Distributions.Beta(2.0, 4.0)
if compute_mode isa ContinuousNormalizingFlows.VectorMode
r = convert.(Float32, rand(data_dist, ndimensions))
r2 = convert.(Float32, rand(data_dist2, ndimensions))
r = rand(data_dist, ndimensions)
r2 = rand(data_dist2, ndimensions)
elseif compute_mode isa ContinuousNormalizingFlows.MatrixMode
r = convert.(Float32, rand(data_dist, ndimensions, ndata))
r2 = convert.(Float32, rand(data_dist2, ndimensions, ndata))
r = rand(data_dist, ndimensions, ndata)
r2 = rand(data_dist2, ndimensions, ndata)
end
df = DataFrames.DataFrame(permutedims(r), :auto)
df2 = DataFrames.DataFrame(permutedims(r2), :auto)
Expand Down Expand Up @@ -124,7 +124,6 @@ Test.@testset verbose = true showtiming = true failfast = false "Smoke Tests" be

Test.@testset verbose = true showtiming = true failfast = false "$adtype on loss" for adtype in
adtypes

Test.@test !isnothing(DifferentiationInterface.gradient(diff_loss, adtype, ps))
Test.@test !isnothing(DifferentiationInterface.gradient(diff2_loss, adtype, r))

Expand Down
4 changes: 1 addition & 3 deletions test/ci_tests/speed_tests.jl
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@@ -1,14 +1,12 @@
Test.@testset verbose = true showtiming = true failfast = false "Speed Tests" begin
Test.@testset verbose = true showtiming = true failfast = false "$compute_mode" for compute_mode in
compute_modes

@show compute_mode

ndata = 2^10
ndimensions = 1
data_dist = Distributions.Beta{Float32}(2.0f0, 4.0f0)
data_dist = Distributions.Beta(2.0, 4.0)
r = rand(data_dist, ndimensions, ndata)
r = convert.(Float32, r)

nvariables = size(r, 1)
icnf = ContinuousNormalizingFlows.ICNF(; nvariables, compute_mode)
Expand Down
12 changes: 6 additions & 6 deletions test/quality_tests/checkby_JET_tests.jl
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Expand Up @@ -13,14 +13,14 @@ Test.@testset verbose = true showtiming = true failfast = false "CheckByJET" beg

ndata = 4
ndimensions = 2
data_dist = Distributions.Beta{Float32}(2.0f0, 4.0f0)
data_dist2 = Distributions.Beta{Float32}(2.0f0, 4.0f0)
data_dist = Distributions.Beta(2.0, 4.0)
data_dist2 = Distributions.Beta(2.0, 4.0)
if compute_mode isa ContinuousNormalizingFlows.VectorMode
r = convert.(Float32, rand(data_dist, ndimensions))
r2 = convert.(Float32, rand(data_dist2, ndimensions))
r = rand(data_dist, ndimensions)
r2 = rand(data_dist2, ndimensions)
elseif compute_mode isa ContinuousNormalizingFlows.MatrixMode
r = convert.(Float32, rand(data_dist, ndimensions, ndata))
r2 = convert.(Float32, rand(data_dist2, ndimensions, ndata))
r = rand(data_dist, ndimensions, ndata)
r2 = rand(data_dist2, ndimensions, ndata)
end
nvariables = size(r, 1)

Expand Down
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