Wrap constant arg in XLAExportInterpreter (#6460)

test/stablehlo/test_xla_export_interpreter.py

@@ -0,0 +1,47 @@
+import re
+import sys
+import unittest
+
+import torch
+import torch_xla
+import torch_xla.core.xla_model as xm
+from torch_xla.stablehlo import exported_program_to_stablehlo
+
+device = xm.xla_device()
+
+
+class XLAExportInterpreterTest(unittest.TestCase):
+
+  def test_constant_wrapping(self):
+
+    class M(torch.nn.Module):
+
+      def forward(self, x):
+        return 1.0 - x
+
+    ep = torch.export.export(M(), (torch.rand(2, 3),))
+    ep = ep.run_decompositions()
+    shlo_module = exported_program_to_stablehlo(ep)
+    shlo_text = shlo_module.get_stablehlo_text()
+    self.assertTrue(
+        re.search(r'stablehlo.constant.* : tensor<2x3xf32>', shlo_text)
+        is not None)
+
+  def test_constant_wrapping_scalar_variant(self):
+
+    class M(torch.nn.Module):
+
+      def forward(self, x):
+        return torch.ops.aten.rsub(x, 1.0)
+
+    ep = torch.export.export(M(), (torch.rand(2, 3),))
+    shlo_module = exported_program_to_stablehlo(ep)
+    shlo_text = shlo_module.get_stablehlo_text()
+    self.assertTrue(
+        re.search(r'stablehlo.constant.* : tensor<2x3xf32>', shlo_text)
+        is not None)
+
+
+if __name__ == '__main__':
+  test = unittest.main()
+  sys.exit(0 if test.result.wasSuccessful() else 1)

torch_xla/stablehlo.py

@@ -233,6 +233,23 @@ def call_function(self, target, args: Tuple, kwargs: Dict) -> Any:
     new_kwargs = dict(kwargs)
     if 'device' in kwargs:
       new_kwargs['device'] = self._device
+    # If the op spec expects a `Tensor` input, we wrap the python float/int
+    # to a torch.tensor. The dtype for float respects
+    # `torch.get_default_dtype`. Without this wrapping, the python float
+    # will be wrapped before it enters dispatcher, and it doesn't respect
+    # the global default dtype.
+    if hasattr(target, '_schema'):
+      # Note: Use `_disable_current_modes` to alwasys create constant tensor.
+      # Under `fake_tensor_mode` a fake tensor will be created. This is not a
+      # use case for XLAExportInterpreter right now, adding to be future-proof.
+      with torch.utils._python_dispatch._disable_current_modes():
+        args_and_specs = tuple(zip(args, target._schema.arguments))
+        args = tuple(
+            map(
+                lambda arg_spec: torch.tensor(arg_spec[0])
+                if isinstance(arg_spec[0], (float, int)) and type(arg_spec[
+                    1].type) == torch.TensorType else arg_spec[0],
+                args_and_specs))
     return super().call_function(target, args, new_kwargs)
 
   def run_node(self, n) -> Any: