Remove some @debug statements (#1499)

Author: devmotion

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.15.6"
+version = "0.15.7"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"

src/contrib/inference/sghmc.jl

@@ -82,29 +82,26 @@ function step(
     η, α = spl.alg.learning_rate, spl.alg.momentum_decay
     spl.info[:eval_num] = 0
 
-    @debug "X-> R..."
     if spl.selector.tag != :default
         link!(vi, spl)
         model(vi, spl)
     end
 
-    @debug "recording old variables..."
+    # Record old variables
     θ, v = vi[spl], spl.info[:v]
     _, grad = gradient_logp(θ, vi, model, spl)
     verifygrad(grad)
 
-    # Implements the update equations from (15) of Chen et al. (2014).
-    @debug "update latent variables and velocity..."
+    # Update latent variables and velocity according to (15) of Chen et al. (2014)
     θ .+= v
     v .= (1 - α) .* v .+ η .* grad .+ rand.(Normal.(zeros(length(θ)), sqrt(2 * η * α)))
 
-    @debug "saving new latent variables..."
+    # Save new latent variables
     vi[spl] = θ
 
-    @debug "R -> X..."
     spl.selector.tag != :default && invlink!(vi, spl)
 
-    @debug "always accept..."
+    # Always accept
     return vi, true
 end
 
@@ -189,30 +186,28 @@ function step(
     spl.info[:i] += 1
     spl.info[:eval_num] = 0
 
-    @debug "compute current step size..."
+    # Compute current step size
     γ = .35
     ϵ_t = spl.alg.ϵ / spl.info[:i]^γ # NOTE: Choose γ=.55 in paper
     mssa = spl.info[:adaptor].ssa
     mssa.state.ϵ = ϵ_t
 
-    @debug "X-> R..."
     if spl.selector.tag != :default
         link!(vi, spl)
         model(vi, spl)
     end
 
-    @debug "recording old variables..."
+    # Record old variables
     θ = vi[spl]
     _, grad = gradient_logp(θ, vi, model, spl)
     verifygrad(grad)
 
-    @debug "update latent variables..."
+    # Update latent variables
     θ .+= ϵ_t .* grad ./ 2 .- rand.(Normal.(zeros(length(θ)), sqrt(ϵ_t)))
 
-    @debug "always accept..."
+    # Always accept
     vi[spl] = θ
 
-    @debug "R -> X..."
     spl.selector.tag != :default && invlink!(vi, spl)
 
     return vi, true

src/inference/gibbs.jl

@@ -153,14 +153,10 @@ function AbstractMCMC.step(
     state::GibbsState;
     kwargs...
 )
-    @debug "Gibbs stepping..."
-
     # Iterate through each of the samplers.
     vi = state.vi
     samplers = state.samplers
     states = map(samplers, state.states) do _sampler, _state
-        @debug "$(typeof(_sampler)) stepping..."
-
         # Update state of current sampler with updated `VarInfo` object.
         current_state = gibbs_update_state(_state, vi)
 

src/inference/hmc.jl

@@ -213,7 +213,6 @@ function DynamicPPL.initialstep(
     end
 
     # Update `vi` based on acceptance
-    @debug "decide whether to accept..."
     if t.stat.is_accept
         vi[spl] = t.z.θ
         setlogp!(vi, t.stat.log_density)
@@ -227,7 +226,6 @@ function DynamicPPL.initialstep(
 
     # If a Gibbs component, transform the values back to the constrained space.
     if spl.selector.tag !== :default
-        @debug "R -> X..."
         invlink!(vi, spl)
     end
 
@@ -252,7 +250,6 @@ function AbstractMCMC.step(
 
     # When a Gibbs component, transform values to the unconstrained space.
     if spl.selector.tag !== :default
-        @debug "X-> R..."
         link!(vi, spl)
         model(rng, vi, spl)
     end
@@ -287,7 +284,6 @@ function AbstractMCMC.step(
     end
 
     # Update `vi` based on acceptance
-    @debug "decide whether to accept..."
     if t.stat.is_accept
         vi[spl] = t.z.θ
         setlogp!(vi, t.stat.log_density)
@@ -302,7 +298,6 @@ function AbstractMCMC.step(
 
     # If a Gibbs component, transform the values back to the constrained space.
     if spl.selector.tag !== :default
-        @debug "R -> X..."
         invlink!(vi, spl)
     end
 

src/variational/advi.jl

@@ -117,7 +117,6 @@ function AdvancedVI.vi(
     q::Bijectors.TransformedDistribution{<:DistributionsAD.TuringDiagMvNormal};
     optimizer = AdvancedVI.TruncatedADAGrad(),
 )
-    @debug "Optimizing ADVI..."
     # Initial parameters for mean-field approx
     μ, σs = StatsBase.params(q)
     θ = vcat(μ, StatsFuns.invsoftplus.(σs))