test: batchnorm layers

test/reactant/layer_tests.jl

@@ -100,7 +100,69 @@ end
     end
 end
 
-@testitem "BatchNorm Layer" tags=[:reactant] setup=[SharedTestSetup] skip=:(Sys.iswindows()) begin
+@testitem "BatchNorm Layer" tags=[:reactant] setup=[
+    SharedTestSetup, SharedReactantLayersTestSetup] skip=:(Sys.iswindows()) begin
     using Reactant, Lux, Random
 
+    @testset "$(mode)" for (mode, atype, dev, ongpu) in MODES
+        if mode == "amdgpu"
+            @warn "Skipping AMDGPU tests for Reactant"
+            continue
+        end
+
+        dev = reactant_device(; force=true)
+
+        if ongpu
+            Reactant.set_default_backend("gpu")
+        else
+            Reactant.set_default_backend("cpu")
+        end
+
+        @testset for track_stats in (true, false), affine in (true, false),
+            act in (identity, tanh)
+
+            model = Chain(
+                Dense(2 => 3, tanh),
+                BatchNorm(3, act; track_stats, affine, init_bias=rand32, init_scale=rand32),
+                Dense(3 => 2)
+            )
+
+            x = rand(Float32, 2, 4)
+            ps, st = Lux.setup(Random.default_rng(), model)
+
+            x_ra = x |> dev
+            ps_ra = ps |> dev
+            st_ra = st |> dev
+
+            y, st2 = model(x, ps, st)
+            y_ra, st2_ra = @jit model(x_ra, ps_ra, st_ra)
+
+            @test y≈y_ra rtol=1e-3 atol=1e-3
+            if track_stats
+                @test st2.layer_2.running_mean≈st2_ra.layer_2.running_mean rtol=1e-3 atol=1e-3
+                @test st2.layer_2.running_var≈st2_ra.layer_2.running_var rtol=1e-3 atol=1e-3
+            end
+
+            # TODO: Check for stablehlo.batch_norm_training once we emit it in LuxLib
+
+            @testset "gradient" begin
+                ∂x, ∂ps = ∇sumabs2_zygote(model, x, ps, st)
+                ∂x_ra, ∂ps_ra = @jit ∇sumabs2_enzyme(model, x_ra, ps_ra, st_ra)
+                @test ∂x_ra≈∂x atol=1e-2 rtol=1e-2
+                @test check_approx(∂ps_ra, ∂ps; atol=1e-2, rtol=1e-2)
+            end
+
+            y2, st3 = model(x, ps, Lux.testmode(st2))
+            y2_ra, st3_ra = @jit model(x_ra, ps_ra, Lux.testmode(st2_ra))
+
+            @test y2≈y2_ra rtol=1e-3 atol=1e-3
+            if track_stats
+                @test st3.layer_2.running_mean≈st3_ra.layer_2.running_mean rtol=1e-3 atol=1e-3
+                @test st3.layer_2.running_var≈st3_ra.layer_2.running_var rtol=1e-3 atol=1e-3
+            end
+
+            hlo = @code_hlo model(x_ra, ps_ra, Lux.testmode(st_ra))
+            @test contains(repr(hlo), "stablehlo.batch_norm_inference")
+        end
+    end
 end