Introduction of model_typed and model_warntype in DebugUtils

Project.toml

@@ -14,6 +14,7 @@ Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
+InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 LogDensityProblemsAD = "996a588d-648d-4e1f-a8f0-a84b347e47b1"

docs/src/api.md

@@ -212,6 +212,13 @@ And some which might be useful to determine certain properties of the model base
 DynamicPPL.has_static_constraints
 ```
 
+For determining whether one might have type instabilities in the model, the following can be useful
+
+```@docs
+DynamicPPL.DebugUtils.model_warntype
+DynamicPPL.DebugUtils.model_codetyped
+```
+
 ## Advanced
 
 ### Variable names

src/debug_utils.jl

@@ -5,6 +5,7 @@
 
 using Random: Random
 using Accessors: Accessors
+using InteractiveUtils: InteractiveUtils
 
 using DocStringExtensions
 using Distributions
@@ -678,4 +679,62 @@
     return all_the_same(transforms)
 end
 
+"""
+    model_warntype(model[, varinfo, context]; optimize=true)
+
+Check the type stability of the model's evaluator, warning about any potential issues.
+
+This simply calls `@code_warntype` on the model's evaluator, filling in internal arguments where needed.
+
+# Arguments
+- `model::Model`: The model to check.
+- `varinfo::AbstractVarInfo`: The varinfo to use when evaluating the model. Default: `VarInfo(model)`.
+- `context::AbstractContext`: The context to use when evaluating the model. Default: [`DefaultContext`](@ref).
+
+# Keyword Arguments
+- `optimize::Bool`: Whether to generate optimized code. Default: `false`.
+"""
+function model_warntype(
+    model::Model,
+    varinfo::AbstractVarInfo=VarInfo(model),
+    context::AbstractContext=DefaultContext();
+    optimize::Bool=false,
+)
+    args, kwargs = DynamicPPL.make_evaluate_args_and_kwargs(model, varinfo, context)
+    return if isempty(kwargs)
+        InteractiveUtils.@code_warntype optimize = optimize model.f(args...)
+    else
+        InteractiveUtils.@code_warntype optimize = optimize model.f(args...; kwargs...)
+    end
+end
+
+"""
+    model_codetyped(model[, varinfo, context]; optimize=true)
+
+Return the type inference for the model's evaluator.
+
+This simply calls `@code_typed` on the model's evaluator, filling in internal arguments where needed.
+
+# Arguments
+- `model::Model`: The model to check.
+- `varinfo::AbstractVarInfo`: The varinfo to use when evaluating the model. Default: `VarInfo(model)`.
+- `context::AbstractContext`: The context to use when evaluating the model. Default: [`DefaultContext`](@ref).
+
+# Keyword Arguments
+- `optimize::Bool`: Whether to generate optimized code. Default: `true`.
+"""
+function model_codetyped(
+    model::Model,
+    varinfo::AbstractVarInfo=VarInfo(model),
+    context::AbstractContext=DefaultContext();
+    optimize::Bool=true,
+)
+    args, kwargs = DynamicPPL.make_evaluate_args_and_kwargs(model, varinfo, context)
+    return if isempty(kwargs)
+        InteractiveUtils.@code_typed optimize = optimize model.f(args...)
+    else
+        InteractiveUtils.@code_typed optimize = optimize model.f(args...; kwargs...)
+    end
+end
+
 end

test/debug_utils.jl

@@ -186,4 +186,14 @@
             @test check_model(model; error_on_failure=true)
         end
     end
+
+    @testset "model_warntype & model_codetyped" begin
+        model = DynamicPPL.TestUtils.DEMO_MODELS[1]
+        codeinfo, retype = DynamicPPL.DebugUtils.model_codetyped(model)
+        @test codeinfo isa Core.CodeInfo
+        @test retype <: Tuple
+
+        # Just make sure the following is runnable.
+        @test (DynamicPPL.DebugUtils.model_warntype(model); true)
+    end
 end