Fix aten.logspace decomposition

test/test_prims.py

@@ -1212,6 +1212,16 @@ def test_unbind(self):
         expect = torch.unbind(a, 1)
         self.assertEqual(actual, expect)
 
+    def test_logspace_with_complex_input(self):
+        actual = refs.logspace(2, 10 + 5j, steps=5)
+        expect = torch.logspace(2, 10 + 5j, steps=5)
+        self.assertEqual(actual, expect)
+
+    def test_linspace_with_complex_input(self):
+        actual = refs.linspace(2, 10 + 5j, steps=5)
+        expect = torch.linspace(2, 10 + 5j, steps=5)
+        self.assertEqual(actual, expect)
+
 
 instantiate_device_type_tests(TestRefs, globals())
 

torch/_refs/__init__.py

@@ -4619,20 +4619,29 @@ def logspace(
         if isinstance(end, FloatLike):
             end = sym_int(end)
 
+    if py_any(isinstance(arg, complex) for arg in (start, end, steps)):
+        default_complex_dtype = utils.corresponding_complex_dtype(
+            torch.get_default_dtype()
+        )
+        dtype = default_complex_dtype
+        _dtype = None  # torch.linspace will update the correct dtype
+    else:
+        _dtype = torch.float64
+
     assert not isinstance(base, complex)  # for mypy
     if base < 0:
         raise NotImplementedError
-    ret = torch.linspace(
+    ret = torch.linspace(  # type: ignore[misc]
         start,
         end,
         steps,  # type: ignore[arg-type]
-        dtype=torch.float64,
+        dtype=_dtype,
         layout=layout,
         device=device,
         pin_memory=pin_memory,
         requires_grad=requires_grad,
     )
-    return _maybe_convert_to_dtype(torch.pow(base, ret), dtype)
+    return _maybe_convert_to_dtype(torch.pow(base, ret), dtype)  # type: ignore[arg-type,return-value]
 
 
 @overload

torch/testing/_internal/common_methods_invocations.py

@@ -1004,7 +1004,7 @@ def sample_inputs_linspace(op, device, dtype, requires_grad, **kwargs):
     yield SampleInput(1, args=(3, 1))
 
 
-def sample_inputs_logpace(op, device, dtype, requires_grad, **kwargs):
+def sample_inputs_logspace(op, device, dtype, requires_grad, **kwargs):
     ends = (-3, 0, 1.2, 2, 4)
     starts = (-2., 0, 1, 2, 4.3)
     nsteps = (0, 1, 2, 4)
@@ -11149,7 +11149,7 @@ def reference_flatten(input, start_dim=0, end_dim=-1):
            supports_out=True,
            supports_autograd=False,
            error_inputs_func=error_inputs_linspace,
-           sample_inputs_func=sample_inputs_logpace,
+           sample_inputs_func=sample_inputs_logspace,
            skips=(
                # Tests that assume input is a tensor or sequence of tensors
                DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_variant_consistency_eager'),