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Merged
merged 9 commits into from
Aug 20, 2020
Merged

Provide AD gradient for MLE/MAP #1369

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da777ba
Use in-place gradients
cpfiffer Aug 3, 2020
fc2f8df
Fixing tests, tidying things up
cpfiffer Aug 4, 2020
284bbdf
Increment patch version
cpfiffer Aug 4, 2020
2ddd480
Change version to 0.14.0, address comments
cpfiffer Aug 5, 2020
5ecc065
Remove hack to fix 2nd order optimizers
cpfiffer Aug 8, 2020
959ff51
Remove redundant FG function
cpfiffer Aug 10, 2020
f431e2b
Merge branch 'master' into csp/explicit-gradient
cpfiffer Aug 20, 2020
53d9134
Add contexts to gradient_logp for Zygote and ReverseDiff
cpfiffer Aug 20, 2020
fa7c59f
One day I'll fix all the files at once
cpfiffer Aug 20, 2020
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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
name = "Turing"
uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
version = "0.13.0"
version = "0.14.0"

[deps]
AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
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12 changes: 8 additions & 4 deletions src/core/ad.jl
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Expand Up @@ -60,6 +60,7 @@ ADBackend(::Val) = error("The requested AD backend is not available. Make sure t
Find the autodifferentiation backend of the algorithm `alg`.
"""
getADbackend(spl::Sampler) = getADbackend(spl.alg)
getADbackend(spl::SampleFromPrior) = ADBackend()()

"""
gradient_logp(
Expand All @@ -77,9 +78,10 @@ function gradient_logp(
θ::AbstractVector{<:Real},
vi::VarInfo,
model::Model,
sampler::Sampler
sampler::AbstractSampler,
ctx::DynamicPPL.AbstractContext = DynamicPPL.DefaultContext()
)
return gradient_logp(getADbackend(sampler), θ, vi, model, sampler)
return gradient_logp(getADbackend(sampler), θ, vi, model, sampler, ctx)
end

"""
Expand All @@ -100,12 +102,13 @@ function gradient_logp(
vi::VarInfo,
model::Model,
sampler::AbstractSampler=SampleFromPrior(),
ctx::DynamicPPL.AbstractContext = DynamicPPL.DefaultContext()
)
# Define function to compute log joint.
logp_old = getlogp(vi)
function f(θ)
new_vi = VarInfo(vi, sampler, θ)
model(new_vi, sampler)
model(new_vi, sampler, ctx)
logp = getlogp(new_vi)
setlogp!(vi, ForwardDiff.value(logp))
return logp
Expand All @@ -127,13 +130,14 @@ function gradient_logp(
vi::VarInfo,
model::Model,
sampler::AbstractSampler = SampleFromPrior(),
ctx::DynamicPPL.AbstractContext = DynamicPPL.DefaultContext()
)
T = typeof(getlogp(vi))

# Specify objective function.
function f(θ)
new_vi = VarInfo(vi, sampler, θ)
model(new_vi, sampler)
model(new_vi, sampler, ctx)
return getlogp(new_vi)
end

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56 changes: 54 additions & 2 deletions src/modes/ModeEstimation.jl
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Expand Up @@ -147,6 +147,50 @@ function (f::OptimLogDensity)(z)
return -DynamicPPL.getlogp(varinfo)
end

function (f::OptimLogDensity)(F, G, z)
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I'm not sure if it's useful to keep this separate definition? It seems we only need f(F, G, H, z), so the implementation could just be included there directly.

spl = DynamicPPL.SampleFromPrior()


if G !== nothing
# Calculate log joint and the gradient
l, g = gradient_logp(
z,
DynamicPPL.VarInfo(f.vi, spl, z),
f.model,
DynamicPPL.SampleFromPrior(),
f.context
)

# Use the negative gradient because we are minimizing.
G[:] = -g

# If F is something, return that since we already have the
# log joint.
if F !== nothing
F = -l
return F
end
end

# No gradient necessary, just return the log joint.
if F !== nothing
F = f(z)
return F
end

return nothing
end

function (f::OptimLogDensity)(F, G, H, z)
# Throw an error if a second order method was used.
if H !== nothing
error("Second order optimization is not yet supported.")
end

# Otherwise, just do the function and gradient info.
return f(F, G, z)
end

"""
ModeResult{
V<:NamedArrays.NamedArray,
Expand Down Expand Up @@ -369,6 +413,11 @@ function _optimize(
args...;
kwargs...
)
# Throw an error if we received a second-order optimizer.
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Do we have to do that? Doesn't Optim just use ForwardDiff (or FD?) to compute the Hessian in this case? If that's the case, then we shouldn't throw an error IMO. It might not be the most efficient approach and would not adhere to the user-provided AD settings but as long as it works we could only print a warning.

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In FD yes; but (for example in the project that I am working on) it could be that users only define custom adjoints for the gradients but not the Hessian. Therefore even the user provides an AD backend, it might not be a great idea if it by default take that for Hessian function.

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You mean we shouldn't even print a warning? Would be fine with me as well.

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Oh, I think throwing an error when some Hessian-required optimizer is received is a great idea, just like what Cameron did here.

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If we want to throw an error if the Hessian is evaluated, I suggest using only_fgh!(f) and implementing f(F, G, H, x) that contains the check

if H !== nothing
    error("second order methods are not supported at the moment")
end

In general, this approach is more flexible, avoids baking in a hardcoded check for a special type of a different package in our implementation, and avoids incorrect and unexpected behaviour for second-order optimization algorithms that don't subtype this specific type (since multiple inheritance is not possible in Julia, that's not an impossible scenario per se).

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It's a bug. I think there might be an issue for it.

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I just found JuliaNLSolvers/Optim.jl#718, I guess that's the related issue.

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Yeah, I have a fix. Sorry to cossio for waiting a year and a half 😬

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I mean, I will tag a fix in an hour or so, so please don't special case with a branch.

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@pkofod pkofod Aug 7, 2020 •
edited
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JuliaRegistries/General#19141

# if optimizer isa Optim.SecondOrderOptimizer
# throw(ArgumentError("Second order optimizers for MLE/MAP are not yet supported."))
# end

# Do some initialization.
spl = DynamicPPL.SampleFromPrior()

Expand All @@ -378,9 +427,12 @@ function _optimize(
link!(f.vi, spl)
init_vals = f.vi[spl]


# Optimize!
M = Optim.optimize(f, init_vals, optimizer, options, args...; kwargs...)
M = if optimizer isa Optim.SecondOrderOptimizer
Optim.optimize(Optim.only_fgh!(f), init_vals, optimizer, options, args...; kwargs...)
else
Optim.optimize(Optim.only_fg!(f), init_vals, optimizer, options, args...; kwargs...)
end

# Warn the user if the optimization did not converge.
if !Optim.converged(M)
Expand Down
14 changes: 5 additions & 9 deletions test/modes/ModeEstimation.jl
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Expand Up @@ -13,19 +13,17 @@ include(dir*"/test/test_utils/AllUtils.jl")
@testset "ModeEstimation.jl" begin
@testset "MLE" begin
Random.seed!(222)
true_value = [0.0625031, 1.75]
true_value = [0.0625, 1.75]

m1 = optimize(gdemo_default, MLE())
m2 = optimize(gdemo_default, MLE(), NelderMead())
m3 = optimize(gdemo_default, MLE(), Newton())
m4 = optimize(gdemo_default, MLE(), true_value, Newton())
m5 = optimize(gdemo_default, MLE(), true_value)
m3 = optimize(gdemo_default, MLE(), true_value, LBFGS())
m4 = optimize(gdemo_default, MLE(), true_value)

@test all(isapprox.(m1.values.array - true_value, 0.0, atol=0.01))
@test all(isapprox.(m2.values.array - true_value, 0.0, atol=0.01))
@test all(isapprox.(m3.values.array - true_value, 0.0, atol=0.01))
@test all(isapprox.(m4.values.array - true_value, 0.0, atol=0.01))
@test all(isapprox.(m5.values.array - true_value, 0.0, atol=0.01))
end

@testset "MAP" begin
Expand All @@ -34,15 +32,13 @@ include(dir*"/test/test_utils/AllUtils.jl")

m1 = optimize(gdemo_default, MAP())
m2 = optimize(gdemo_default, MAP(), NelderMead())
m3 = optimize(gdemo_default, MAP(), Newton())
m4 = optimize(gdemo_default, MAP(), true_value, Newton())
m5 = optimize(gdemo_default, MAP(), true_value)
m3 = optimize(gdemo_default, MAP(), true_value, LBFGS())
m4 = optimize(gdemo_default, MAP(), true_value)

@test all(isapprox.(m1.values.array - true_value, 0.0, atol=0.01))
@test all(isapprox.(m2.values.array - true_value, 0.0, atol=0.01))
@test all(isapprox.(m3.values.array - true_value, 0.0, atol=0.01))
@test all(isapprox.(m4.values.array - true_value, 0.0, atol=0.01))
@test all(isapprox.(m5.values.array - true_value, 0.0, atol=0.01))
end

@testset "StatsBase integration" begin
Expand Down