diff --git a/lib/MLDataDevices/Project.toml b/lib/MLDataDevices/Project.toml
index 49b955621..9566c340d 100644
--- a/lib/MLDataDevices/Project.toml
+++ b/lib/MLDataDevices/Project.toml
@@ -1,7 +1,7 @@
 name = "MLDataDevices"
 uuid = "7e8f7934-dd98-4c1a-8fe8-92b47a384d40"
 authors = ["Avik Pal <avikpal@mit.edu> and contributors"]
-version = "1.5.1"
+version = "1.5.2"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
@@ -13,6 +13,7 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 [weakdeps]
 AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+ChainRules = "082447d4-558c-5d27-93f4-14fc19e9eca2"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 GPUArrays = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
@@ -31,6 +32,7 @@ oneAPI = "8f75cd03-7ff8-4ecb-9b8f-daf728133b1b"
 [extensions]
 MLDataDevicesAMDGPUExt = "AMDGPU"
 MLDataDevicesCUDAExt = "CUDA"
+MLDataDevicesChainRulesExt = "ChainRules"
 MLDataDevicesChainRulesCoreExt = "ChainRulesCore"
 MLDataDevicesFillArraysExt = "FillArrays"
 MLDataDevicesGPUArraysExt = "GPUArrays"
@@ -50,6 +52,7 @@ MLDataDevicesoneAPIExt = ["GPUArrays", "oneAPI"]
 AMDGPU = "0.9.6, 1"
 Adapt = "4.1"
 CUDA = "5.2"
+ChainRules = "1.51"
 ChainRulesCore = "1.23"
 Compat = "4.15"
 FillArrays = "1"
diff --git a/lib/MLDataDevices/ext/MLDataDevicesCUDAExt.jl b/lib/MLDataDevices/ext/MLDataDevicesCUDAExt.jl
index 9355b8171..90a5fe733 100644
--- a/lib/MLDataDevices/ext/MLDataDevicesCUDAExt.jl
+++ b/lib/MLDataDevices/ext/MLDataDevicesCUDAExt.jl
@@ -54,6 +54,7 @@ end
 
 # Device Transfer
 Adapt.adapt_storage(::CUDADevice{Nothing}, x::AbstractArray) = CUDA.cu(x)
+
 function Adapt.adapt_storage(to::CUDADevice, x::AbstractArray)
     old_dev = CUDA.device()  # remember the current device
     dev = MLDataDevices.get_device(x)
diff --git a/lib/MLDataDevices/ext/MLDataDevicesChainRulesExt.jl b/lib/MLDataDevices/ext/MLDataDevicesChainRulesExt.jl
new file mode 100644
index 000000000..25b05c01d
--- /dev/null
+++ b/lib/MLDataDevices/ext/MLDataDevicesChainRulesExt.jl
@@ -0,0 +1,16 @@
+module MLDataDevicesChainRulesExt
+
+using Adapt: Adapt
+using MLDataDevices: CPUDevice, CUDADevice, AMDGPUDevice, MetalDevice, oneAPIDevice,
+                     ReactantDevice
+using ChainRules: OneElement
+
+Adapt.adapt_structure(::CPUDevice, x::OneElement) = x
+
+for dev in (CUDADevice, AMDGPUDevice, MetalDevice, oneAPIDevice, ReactantDevice,
+    CUDADevice{Nothing}, AMDGPUDevice{Nothing})
+    # use `@eval` to avoid ambiguity with adapt_storage(::CUDADevice, ::AbstractArray)
+    @eval Adapt.adapt_structure(to::$(dev), x::OneElement) = Adapt.adapt(to, collect(x))
+end
+
+end
diff --git a/lib/MLDataDevices/ext/MLDataDevicesZygoteExt.jl b/lib/MLDataDevices/ext/MLDataDevicesZygoteExt.jl
index 1b705c582..66a363d55 100644
--- a/lib/MLDataDevices/ext/MLDataDevicesZygoteExt.jl
+++ b/lib/MLDataDevices/ext/MLDataDevicesZygoteExt.jl
@@ -1,10 +1,16 @@
 module MLDataDevicesZygoteExt
 
 using Adapt: Adapt
-using MLDataDevices: AbstractDevice, CPUDevice
+using MLDataDevices: CPUDevice, CUDADevice, AMDGPUDevice, MetalDevice, oneAPIDevice,
+                     ReactantDevice
 using Zygote: OneElement
 
 Adapt.adapt_structure(::CPUDevice, x::OneElement) = x
-Adapt.adapt_structure(to::AbstractDevice, x::OneElement) = Adapt.adapt(to, collect(x))
+
+for dev in (CUDADevice, AMDGPUDevice, MetalDevice, oneAPIDevice, ReactantDevice,
+    CUDADevice{Nothing}, AMDGPUDevice{Nothing})
+    # use `@eval` to avoid ambiguity with adapt_storage(::CUDADevice, ::AbstractArray)
+    @eval Adapt.adapt_structure(to::$(dev), x::OneElement) = Adapt.adapt(to, collect(x))
+end
 
 end
diff --git a/lib/MLDataDevices/test/misc_tests.jl b/lib/MLDataDevices/test/misc_tests.jl
index 5ece810bf..55265ada3 100644
--- a/lib/MLDataDevices/test/misc_tests.jl
+++ b/lib/MLDataDevices/test/misc_tests.jl
@@ -222,6 +222,21 @@ end
     @test only(Zygote.gradient(x -> sum(abs2, gdev(x)), x')) isa Matrix{Float64}
 end
 
+@testset "Zygote and ChainRules OneElement #1016" begin
+    using Zygote
+
+    cdev = cpu_device()
+    gdev = gpu_device()
+
+    g = only(Zygote.gradient(x -> cdev(2 .* gdev(x))[1], Float32[1, 2, 3]))
+    @test g isa Vector{Float32}
+
+    g = only(Zygote.gradient(
+        x -> cdev(gdev(x) * gdev(x))[1, 2], Float32[1 2 3; 4 5 6; 7 8 9]
+    ))
+    @test g isa Matrix{Float32}
+end
+
 @testset "OneHotArrays" begin
     using OneHotArrays