diff --git a/codegen/xla_native_functions.yaml b/codegen/xla_native_functions.yaml
index bd57e18b1a3..e0d917db684 100644
--- a/codegen/xla_native_functions.yaml
+++ b/codegen/xla_native_functions.yaml
@@ -192,6 +192,7 @@ supported:
   - floor_divide
   - fmod.Scalar
   - fmod.Tensor
+  - full
   - gather
   - gelu
   - gelu_backward
diff --git a/test/cpp/test_aten_xla_tensor_1.cpp b/test/cpp/test_aten_xla_tensor_1.cpp
index a5db45185f7..e95651604d7 100644
--- a/test/cpp/test_aten_xla_tensor_1.cpp
+++ b/test/cpp/test_aten_xla_tensor_1.cpp
@@ -139,8 +139,7 @@ TEST_F(AtenXlaTensorTest, TestFull) {
   });
 
   ExpectCounterNotChanged("aten::.*", cpp_test::GetIgnoredCounters());
-  ExpectCounterChanged("xla::empty", cpp_test::GetIgnoredCounters());
-  ExpectCounterChanged("xla::fill_", cpp_test::GetIgnoredCounters());
+  ExpectCounterChanged("xla::full", cpp_test::GetIgnoredCounters());
 }
 
 TEST_F(AtenXlaTensorTest, TestFullLike) {
diff --git a/torch_xla/csrc/aten_xla_type.cpp b/torch_xla/csrc/aten_xla_type.cpp
index fa2197093dc..f6d73f8ca1d 100644
--- a/torch_xla/csrc/aten_xla_type.cpp
+++ b/torch_xla/csrc/aten_xla_type.cpp
@@ -1331,6 +1331,24 @@ at::Tensor XLANativeFunctions::fmod(const at::Tensor& self,
                     });
 }
 
+at::Tensor XLANativeFunctions::full(at::IntArrayRef size,
+                                    const at::Scalar& fill_value,
+                                    c10::optional<at::ScalarType> dtype,
+                                    c10::optional<at::Layout> layout,
+                                    c10::optional<at::Device> device,
+                                    c10::optional<bool> pin_memory) {
+  TORCH_LAZY_FN_COUNTER("xla::");
+  // Fall back to CPU if layout or pin_memory are not default
+  if (layout.value_or(at::Layout::Strided) != at::Layout::Strided ||
+      pin_memory.value_or(false)) {
+    return at::native::call_fallback_fn<&xla_cpu_fallback, ATEN_OP(full)>::call(
+        size, fill_value, dtype, layout, device, pin_memory);
+  }
+  return bridge::AtenFromXlaTensor(tensor_methods::full(
+      absl::Span<const int64_t>(size), fill_value,
+      GetXlaDeviceOrCurrent(device), at::dtype_or_default(dtype)));
+}
+
 at::Tensor XLANativeFunctions::gather(const at::Tensor& self, int64_t dim,
                                       const at::Tensor& index,
                                       bool /* sparse_grad */) {