From 0a54b2b1b8e0e9a895da09bfba352d1a08a44259 Mon Sep 17 00:00:00 2001
From: JackCaoG <59073027+JackCaoG@users.noreply.github.com>
Date: Wed, 1 May 2024 15:24:30 -0700
Subject: [PATCH] Fix torch.full scalar type (#7010)

---
 test/test_ops.py                 |  4 ++--
 torch_xla/csrc/aten_xla_type.cpp | 15 ++++++++++++---
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/test/test_ops.py b/test/test_ops.py
index 12b874593bd..3b098e85f93 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'),
@@ -393,7 +394,7 @@ def _cpu(t):
           return tuple(map(to_cpu, x))
         elif isinstance(x, dict):
           return {k: to_cpu(v) for k, v in x.items()}
-        elif isinstance(x, (numbers.Number, bool, str)):
+        elif isinstance(x, (numbers.Number, bool, str, torch.dtype)):
           return x
 
         # Passthrough None because some functions wrapped with type promotion
@@ -426,5 +427,4 @@ def test_reference_eager(self, device, dtype, op):
 instantiate_device_type_tests(TestOpInfo, globals())
 
 if __name__ == '__main__':
-  #run_tests()
   unittest.main()
diff --git a/torch_xla/csrc/aten_xla_type.cpp b/torch_xla/csrc/aten_xla_type.cpp
index 56a69ca1e05..8464d1320c2 100644
--- a/torch_xla/csrc/aten_xla_type.cpp
+++ b/torch_xla/csrc/aten_xla_type.cpp
@@ -1470,9 +1470,18 @@ 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), at::dtype_or_default(dtype)));
+  at::ScalarType intend_dtype;
+  if (dtype || fill_value.isFloatingPoint()) {
+    // Respect the dtype if it is being explictlly passed in.
+    // 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,