Improvements to benchmark outputs

benchmarks/benchmarks.jl

@@ -98,12 +98,15 @@ function run(; to_json=false)
     }[]
 
     for (model_name, model, varinfo_choice, adbackend, islinked) in chosen_combinations
-        @info "Running benchmark for $model_name"
+        @info "Running benchmark for $model_name, $varinfo_choice, $adbackend, $islinked"
         relative_eval_time, relative_ad_eval_time = try
             results = benchmark(model, varinfo_choice, adbackend, islinked)
+            @info " t(eval) = $(results.primal_time)"
+            @info " t(grad) = $(results.grad_time)"
             (results.primal_time / reference_time),
             (results.grad_time / results.primal_time)
         catch e
+            @info "benchmark errored: $e"
             missing, missing
         end
         push!(
@@ -155,18 +158,33 @@ function combine(head_filename::String, base_filename::String)
     all_testcases = union(Set(keys(head_testcases)), Set(keys(base_testcases)))
     @info "$(length(all_testcases)) unique test cases found"
     sorted_testcases = sort(
-        collect(all_testcases); by=(c -> (c.model_name, c.ad_backend, c.varinfo, c.linked))
+        collect(all_testcases); by=(c -> (c.model_name, c.linked, c.varinfo, c.ad_backend))
     )
     results_table = Tuple{
-        String,Int,String,String,Bool,String,String,String,String,String,String
+        String,
+        Int,
+        String,
+        String,
+        Bool,
+        String,
+        String,
+        String,
+        String,
+        String,
+        String,
+        String,
+        String,
+        String,
     }[]
+    sublabels = ["base", "this PR", "speedup"]
     results_colnames = [
         [
             EmptyCells(5),
             MultiColumn(3, "t(eval) / t(ref)"),
             MultiColumn(3, "t(grad) / t(eval)"),
+            MultiColumn(3, "t(grad) / t(ref)"),
         ],
-        [colnames[1:5]..., "base", "this PR", "speedup", "base", "this PR", "speedup"],
+        [colnames[1:5]..., sublabels..., sublabels..., sublabels...],
     ]
     sprint_float(x::Float64) = @sprintf("%.2f", x)
     sprint_float(m::Missing) = "err"
@@ -183,6 +201,10 @@ function combine(head_filename::String, base_filename::String)
         # Finally that lets us do this division safely
         speedup_eval = base_eval / head_eval
         speedup_grad = base_grad / head_grad
+        # As well as this multiplication, which is t(grad) / t(ref)
+        head_grad_vs_ref = head_grad * head_eval
+        base_grad_vs_ref = base_grad * base_eval
+        speedup_grad_vs_ref = base_grad_vs_ref / head_grad_vs_ref
         push!(
             results_table,
             (
@@ -197,6 +219,9 @@ function combine(head_filename::String, base_filename::String)
                 sprint_float(base_grad),
                 sprint_float(head_grad),
                 sprint_float(speedup_grad),
+                sprint_float(base_grad_vs_ref),
+                sprint_float(head_grad_vs_ref),
+                sprint_float(speedup_grad_vs_ref),
             ),
         )
     end
@@ -212,7 +237,10 @@ function combine(head_filename::String, base_filename::String)
             backend=:text,
             fit_table_in_display_horizontally=false,
             fit_table_in_display_vertically=false,
-            table_format=TextTableFormat(; horizontal_line_at_merged_column_labels=true),
+            table_format=TextTableFormat(;
+                horizontal_line_at_merged_column_labels=true,
+                horizontal_lines_at_data_rows=collect(3:3:length(results_table)),
+            ),
         )
         println("```")
     end

src/test_utils/ad.jl

@@ -5,7 +5,13 @@ using Chairmarks: @be
 import DifferentiationInterface as DI
 using DocStringExtensions
 using DynamicPPL:
-    Model, LogDensityFunction, VarInfo, AbstractVarInfo, getlogjoint_internal, link
+    DynamicPPL,
+    Model,
+    LogDensityFunction,
+    VarInfo,
+    AbstractVarInfo,
+    getlogjoint_internal,
+    link
 using LogDensityProblems: logdensity, logdensity_and_gradient
 using Random: AbstractRNG, default_rng
 using Statistics: median
@@ -298,7 +304,9 @@ function run_ad(
 
     # Benchmark
     grad_time, primal_time = if benchmark
+        logdensity(ldf, params)  # Warm-up
         primal_benchmark = @be logdensity($ldf, $params)
+        logdensity_and_gradient(ldf, params)  # Warm-up
         grad_benchmark = @be logdensity_and_gradient($ldf, $params)
         median_primal = median(primal_benchmark).time
         median_grad = median(grad_benchmark).time