Skip CUDA tests when no CUDA devices are found. (#436)

* Update cuda.jl

* Apply suggestions from code review

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

---------

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: yebai

test/CUDA/cuda.jl

@@ -11,47 +11,50 @@ using LogDensityProblems
 include(joinpath(@__DIR__, "..", "common.jl"))
 
 @testset "AdvancedHMC GPU" begin
-    n_chains = 1000
-    n_samples = 1000
-    dim = 5
-
-    T = Float32
-    m, s, θ₀ = zeros(T, dim), ones(T, dim), rand(T, dim, n_chains)
-    m, s, θ₀ = CuArray(m), CuArray(s), CuArray(θ₀)
-
-    target = Gaussian(m, s)
-    metric = UnitEuclideanMetric(T, size(θ₀))
-    ℓπ, ∇ℓπ = get_ℓπ(target), get_∇ℓπ(target)
-    hamiltonian = Hamiltonian(metric, ℓπ, ∇ℓπ)
-    integrator = Leapfrog(one(T) / 5)
-    proposal = HMCKernel(Trajectory{EndPointTS}(integrator, FixedNSteps(5)))
-
-    samples, stats = sample(hamiltonian, proposal, θ₀, n_samples)
+    if CUDA.functional()
+        n_chains = 1000
+        n_samples = 1000
+        dim = 5
+        T = Float32
+        m, s, θ₀ = zeros(T, dim), ones(T, dim), rand(T, dim, n_chains)
+        m, s, θ₀ = CuArray(m), CuArray(s), CuArray(θ₀)
+        target = Gaussian(m, s)
+        metric = UnitEuclideanMetric(T, size(θ₀))
+        ℓπ, ∇ℓπ = get_ℓπ(target), get_∇ℓπ(target)
+        hamiltonian = Hamiltonian(metric, ℓπ, ∇ℓπ)
+        integrator = Leapfrog(one(T) / 5)
+        proposal = HMCKernel(Trajectory{EndPointTS}(integrator, FixedNSteps(5)))
+        samples, stats = sample(hamiltonian, proposal, θ₀, n_samples)
+    else
+        println("GPU tests are skipped because no CUDA devices are found.")
+    end
 end
 
 @testset "PhasePoint GPU" begin
-    for T in [Float32, Float64]
-        function init_z1()
-            return PhasePoint(
-                CuArray([T(NaN) T(NaN)]),
-                CuArray([T(NaN) T(NaN)]),
-                DualValue(CuArray(zeros(T, 2)), CuArray(zeros(T, 1, 2))),
-                DualValue(CuArray(zeros(T, 2)), CuArray(zeros(T, 1, 2))),
-            )
+    if CUDA.functional()
+        for T in [Float32, Float64]
+            function init_z1()
+                return PhasePoint(
+                    CuArray([T(NaN) T(NaN)]),
+                    CuArray([T(NaN) T(NaN)]),
+                    DualValue(CuArray(zeros(T, 2)), CuArray(zeros(T, 1, 2))),
+                    DualValue(CuArray(zeros(T, 2)), CuArray(zeros(T, 1, 2))),
+                )
+            end
+            function init_z2()
+                return PhasePoint(
+                    CuArray([T(Inf) T(Inf)]),
+                    CuArray([T(Inf) T(Inf)]),
+                    DualValue(CuArray(zeros(T, 2)), CuArray(zeros(T, 1, 2))),
+                    DualValue(CuArray(zeros(T, 2)), CuArray(zeros(T, 1, 2))),
+                )
+            end
+            z1 = init_z1()
+            z2 = init_z2()
+            @test z1.ℓπ.value == z2.ℓπ.value
+            @test z1.ℓκ.value == z2.ℓκ.value
         end
-        function init_z2()
-            return PhasePoint(
-                CuArray([T(Inf) T(Inf)]),
-                CuArray([T(Inf) T(Inf)]),
-                DualValue(CuArray(zeros(T, 2)), CuArray(zeros(T, 1, 2))),
-                DualValue(CuArray(zeros(T, 2)), CuArray(zeros(T, 1, 2))),
-            )
-        end
-
-        z1 = init_z1()
-        z2 = init_z2()
-
-        @test z1.ℓπ.value == z2.ℓπ.value
-        @test z1.ℓκ.value == z2.ℓκ.value
+    else
+        println("GPU tests are skipped because no CUDA devices are found.")
     end
 end