add type hint for julia 1.10

to make loss_gf inferred

Author: bgctw

src/elbo.jl

@@ -246,15 +246,13 @@ function compute_priors_logdensity(priorsP, priorsM, θP, θMs, zero_prior_logde
             #TRET = Base.return_types(logpdf_tv_sum, Tuple{typeof(priorsM[i]), typeof(θMs[:,i])})
         end
     end
-    nlMs_sum = sum(f_col, 1:length(priorsM))
+    nlMs_sum = sum(f_col, 1:length(priorsM))::typeof(nlP0) # not type inferred in julia 1.10
     neg_log_prior_i = nlP0 - nlMs_sum
     if !isfinite(neg_log_prior_i)
         @show neg_log_prior_i, nlP0
         @show θMs
         @show priorsM
         error("inspect non-finite priors")
-        i_par = 2
-        priorMi, θMi = priorsM[i_par], eachcol(θMs)[2]
     end
     neg_log_prior_i
 end

src/gf.jl

@@ -277,8 +277,10 @@ function get_loss_gf(g, transM, transP, f, py,
             logpdf_tv = (prior, θ::AbstractVector) -> begin
                 map(Base.Fix1(logpdf, prior), θ)::Vector{eltype(θP_pred)}
             end
-            neg_log_prior = compute_priors_logdensity(priorsP, priorsM, θP_pred, θMs_pred,
-                is_omit_priors, zero_prior_logdensity)
+            neg_log_prior = 
+                # @descend_code_warntype (
+                compute_priors_logdensity(priorsP, priorsM, θP_pred, θMs_pred,
+                    is_omit_priors, zero_prior_logdensity)
             if !isfinite(neg_log_prior)
                 @info "loss_gf: encountered non-finite prior density"
                 @show θP_pred, θMs_pred, ϕc.ϕP

test/test_HybridProblem.jl

@@ -171,9 +171,12 @@ test_without_flux = (scenario) -> begin
         (_xM, _xP, _y_o, _y_unc, _i_sites) = first(train_loader)
         #l1 = loss_gf(p0, _xM, _xP, _y_o, _y_unc, _i_sites; is_testmode = false)
 
+        #using ShareAdd
+        #@usingany Cthulhu
         l1 = @inferred (
             # @descend_code_warntype (
-            loss_gf(p0, _xM, _xP, _y_o, _y_unc, _i_sites; is_testmode = true))
+            loss_gf(p0, _xM, _xP, _y_o, _y_unc, _i_sites; is_testmode = true)
+            )
         tld = first(train_loader)
         gr = Zygote.gradient(p -> loss_gf(p, tld...; is_testmode = false)[1], CA.getdata(p0))
         @test gr[1] isa Vector
@@ -198,6 +201,7 @@ test_without_flux = (scenario) -> begin
 end
 
 #test_without_flux(Val((:MeanHVIApproximation,)))  # not used in loss_gf
+#scenario=Val((:default,))
 test_without_flux(Val((:default,)))
 test_without_flux(Val((:covarK2,)))