Remove runmodel!

Author: devmotion

src/DynamicPPL.jl

@@ -68,7 +68,6 @@ export  VarName,
         getargnames,
         getdefaults,
         getgenerator,
-        runmodel!,
 # Samplers
         Sampler,
         SampleFromPrior,

src/model.jl

@@ -109,24 +109,25 @@ function Model{missings}(
     return Model{missings}(model.f, args, modelgen)
 end
 
+"""
+    (model::Model)([spl = SampleFromPrior(), ctx = DefaultContext()])
 
+Sample from `model` using the sampler `spl`.
+"""
 function (model::Model)(
-    vi::AbstractVarInfo=VarInfo(),
     spl::AbstractSampler=SampleFromPrior(),
     ctx::AbstractContext=DefaultContext()
 )
-    return model.f(model, vi, spl, ctx)
+    return model(VarInfo(), spl, ctx)
 end
 
-
 """
-    runmodel!(model::Model, vi::AbstractVarInfo[, spl::AbstractSampler, ctx::AbstractContext])
+    (model::Model)(vi::AbstractVarInfo[, spl = SampleFromPrior(), ctx = DefaultContext()])
 
 Sample from `model` using the sampler `spl` storing the sample and log joint probability in `vi`.
 Resets the `vi` and increases `spl`s `state.eval_num`.
 """
-function runmodel!(
-    model::Model,
+function (model::Model)(
     vi::AbstractVarInfo,
     spl::AbstractSampler=SampleFromPrior(),
     ctx::AbstractContext=DefaultContext()
@@ -135,8 +136,7 @@ function runmodel!(
     if has_eval_num(spl)
         spl.state.eval_num += 1
     end
-    model(vi, spl, ctx)
-    return vi
+    return model.f(model, vi, spl, ctx)
 end
 
 

src/varinfo.jl

@@ -107,7 +107,7 @@ const TypedVarInfo = VarInfo{<:NamedTuple}
 
 function VarInfo(model::Model, ctx = DefaultContext())
     vi = VarInfo()
-    runmodel!(model, vi, SampleFromPrior(), ctx)
+    model(vi, SampleFromPrior(), ctx)
     return TypedVarInfo(vi)
 end
 

test/Turing/Turing.jl

@@ -16,7 +16,7 @@ using Markdown, Libtask, MacroTools
 using Tracker: Tracker
 
 import Base: ~, ==, convert, hash, promote_rule, rand, getindex, setindex!
-import DynamicPPL: getspace, runmodel!
+import DynamicPPL: getspace
 
 const PROGRESS = Ref(true)
 function turnprogress(switch::Bool)

test/Turing/contrib/inference/dynamichmc.jl

@@ -60,11 +60,11 @@ function AbstractMCMC.sample_init!(
         gradient_logp(x, spl.state.vi, model, spl)
     end
 
-    runmodel!(model, spl.state.vi, SampleFromUniform())
+    model(spl.state.vi, SampleFromUniform())
 
     if spl.selector.tag == :default
         link!(spl.state.vi, spl)
-        runmodel!(model, spl.state.vi, spl)
+        model(spl.state.vi, spl)
     end
 
     # Set the parameters to a starting value.
@@ -145,4 +145,4 @@ function AbstractMCMC.psample(
     end
     return AbstractMCMC.psample(rng, model, Sampler(alg, model), N, n_chains;
                                 chain_type=chain_type, progress=false, kwargs...)
-end
+end

test/Turing/contrib/inference/sghmc.jl

@@ -85,7 +85,7 @@ function step(
     Turing.DEBUG && @debug "X-> R..."
     if spl.selector.tag != :default
         link!(vi, spl)
-        runmodel!(model, vi, spl)
+        model(vi, spl)
     end
 
     Turing.DEBUG && @debug "recording old variables..."
@@ -198,7 +198,7 @@ function step(
     Turing.DEBUG && @debug "X-> R..."
     if spl.selector.tag != :default
         link!(vi, spl)
-        runmodel!(model, vi, spl)
+        model(vi, spl)
     end
 
     Turing.DEBUG && @debug "recording old variables..."

test/Turing/core/Core.jl

@@ -6,7 +6,7 @@ using Distributions, LinearAlgebra
 using ..Utilities, Reexport
 using Tracker: Tracker
 using ..Turing: Turing
-using DynamicPPL: Model, runmodel!,
+using DynamicPPL: Model,
     AbstractSampler, Sampler, SampleFromPrior
 using LinearAlgebra: copytri!
 using Bijectors: PDMatDistribution

test/Turing/core/ad.jl

@@ -92,7 +92,8 @@ function gradient_logp(
     logp_old = getlogp(vi)
     function f(θ)
         new_vi = VarInfo(vi, sampler, θ)
-        logp = getlogp(runmodel!(model, new_vi, sampler))
+        model(new_vi, sampler)
+        logp = getlogp(new_vi)
         setlogp!(vi, ForwardDiff.value(logp))
         return logp
     end
@@ -119,7 +120,8 @@ function gradient_logp(
     # Specify objective function.
     function f(θ)
         new_vi = VarInfo(vi, sampler, θ)
-        return getlogp(runmodel!(model, new_vi, sampler))
+        model(new_vi, sampler)
+        return getlogp(new_vi)
     end
 
     # Compute forward and reverse passes.

test/Turing/core/compat/zygote.jl

@@ -16,7 +16,8 @@ function gradient_logp(
     # Specify objective function.
     function f(θ)
         new_vi = VarInfo(vi, sampler, θ)
-        return getlogp(runmodel!(model, new_vi, sampler))
+        model(new_vi, sampler)
+        return getlogp(new_vi)
     end
 
     # Compute forward and reverse passes.

test/Turing/inference/Inference.jl

@@ -4,7 +4,7 @@ using ..Core, ..Utilities
 using DynamicPPL: Metadata, _tail, VarInfo, TypedVarInfo, 
     islinked, invlink!, getlogp, tonamedtuple, VarName, getsym, vectorize, 
     settrans!, _getvns, getdist, split_var_str, CACHERESET, AbstractSampler,
-    Model, runmodel!, Sampler, SampleFromPrior, SampleFromUniform,
+    Model, Sampler, SampleFromPrior, SampleFromUniform,
     Selector, AbstractSamplerState, DefaultContext, PriorContext,
     LikelihoodContext, MiniBatchContext, set_flag!, unset_flag!, NamedDist, NoDist
 using Distributions, Libtask, Bijectors