diff --git a/test/test_ops.py b/test/test_ops.py
index 12b874593bd..a7366bc7510 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -225,6 +225,7 @@ def __new__(cls, name, variant_test_name=""):
             AllowedOpInfoEntry('norm', 'fro'),
             AllowedOpInfoEntry('special.erfcx'),
             AllowedOpInfoEntry('_native_batch_norm_legit'),
+            AllowedOpInfoEntry('full'),
 
             # Duplicate Redundant entries for this test.
             # AllowedOpInfoEntry('polygamma', 'polygamma_n_1'),
diff --git a/torch_xla/csrc/aten_xla_type.cpp b/torch_xla/csrc/aten_xla_type.cpp
index f699c2c1c9f..d7bf4d8cd7b 100644
--- a/torch_xla/csrc/aten_xla_type.cpp
+++ b/torch_xla/csrc/aten_xla_type.cpp
@@ -1470,9 +1470,20 @@ at::Tensor XLANativeFunctions::full(at::IntArrayRef size,
     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), dtype ? *dtype : fill_value.type()));
+  at::ScalarType intend_dtype;
+  if (dtype) {
+    // Respect the dtype if it is being explictlly passed in.
+    intend_dtype = *dtype;
+  } else if (fill_value.isFloatingPoint()) {
+    // All python scalar will be passed in as float64 to the backend, but the
+    // default behavior for pytorch is to return a float32 tensor in this case.
+    intend_dtype = at::dtype_or_default(dtype);
+  } else {
+    intend_dtype = fill_value.type();
+  }
+  return bridge::AtenFromXlaTensor(
+      tensor_methods::full(absl::Span<const int64_t>(size), fill_value,
+                           GetXlaDeviceOrCurrent(device), intend_dtype));
 }
 
 at::Tensor XLANativeFunctions::gather(const at::Tensor& self, int64_t dim,