test: skip some Enzyme tests

LuxDL · Nov 5, 2024 · 318055f · 318055f
1 parent 58369a5
commit 318055f
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Showing 4 changed files with 7 additions and 5 deletions.
diff --git a/lib/LuxLib/test/common_ops/conv_tests.jl b/lib/LuxLib/test/common_ops/conv_tests.jl
@@ -63,7 +63,7 @@ function run_conv_testing(gen_f::Function, activation, kernel, stride, padding,
         (w, x, b) -> __f(activation, w, x, b, cdims)
     end
 
-    skip_backends = []
+    skip_backends = [AutoEnzyme()]
     mp = Tx != Tw
     mp && push!(skip_backends, AutoReverseDiff())
     ((mp && ongpu) || (mode == "amdgpu" && (Tx == Float64 || Tw == Float64))) &&

diff --git a/lib/LuxLib/test/normalization/groupnorm_tests.jl b/lib/LuxLib/test/normalization/groupnorm_tests.jl
@@ -74,7 +74,8 @@ function run_groupnorm_testing(T, sz, groups, affine, act, aType, mode, ongpu)
 
     if affine
         __f = (args...) -> sum(groupnorm(args..., groups, act, epsilon))
-        @test_gradients(__f, x, scale, bias; atol, rtol, soft_fail)
+        @test_gradients(__f, x, scale, bias; atol, rtol, soft_fail,
+            skip_backends=[AutoEnzyme()])
     end
 end
 

diff --git a/lib/LuxLib/test/normalization/instancenorm_tests.jl b/lib/LuxLib/test/normalization/instancenorm_tests.jl
@@ -66,7 +66,7 @@ function run_instancenorm_testing(gen_f, T, sz, training, act, aType, mode, ongp
         __f = (args...) -> sum(first(instancenorm(
             args..., rm, rv, training, act, T(0.1), epsilon)))
         soft_fail = fp16 ? fp16 : [AutoFiniteDiff()]
-        skip_backends = (Sys.iswindows() && fp16) ? [AutoEnzyme()] : []
+        skip_backends = [AutoEnzyme()]
         @test_gradients(__f, x, scale, bias; atol, rtol, soft_fail, skip_backends)
     end
 end

diff --git a/lib/LuxLib/test/normalization/layernorm_tests.jl b/lib/LuxLib/test/normalization/layernorm_tests.jl
@@ -58,10 +58,11 @@ function run_layernorm_testing_core(
     soft_fail = fp16 ? fp16 : [AutoFiniteDiff()]
     if affine_shape !== nothing
         __f = (args...) -> sum(_f(args...))
-        @test_gradients(__f, x, scale, bias; atol, rtol, soft_fail)
+        @test_gradients(__f, x, scale, bias; atol, rtol, soft_fail,
+            skip_backends=[AutoEnzyme()])
     else
         __f = x -> sum(_f(x, scale, bias))
-        @test_gradients(__f, x; atol, rtol, soft_fail)
+        @test_gradients(__f, x; atol, rtol, soft_fail, skip_backends=[AutoEnzyme()])
     end
 
     if anonact !== act