diff --git a/aten/src/ATen/native/Lerp.cpp b/aten/src/ATen/native/Lerp.cpp
index ecfdd75e559..c11838a8007 100644
--- a/aten/src/ATen/native/Lerp.cpp
+++ b/aten/src/ATen/native/Lerp.cpp
@@ -16,10 +16,16 @@ TORCH_META_FUNC(lerp_Tensor)(
     const Tensor& self, const Tensor& end, const Tensor& weight) {
   TORCH_CHECK(self.dtype() == end.dtype(), "expected dtype ", self.dtype(),
               " for `end` but got dtype ", end.dtype());
-  TORCH_CHECK(self.dtype() == weight.dtype(), "expected dtype ", self.dtype(),
-              " for `weight` but got dtype ", weight.dtype());
+  bool promote_weight = weight.dim() == 0;
+  if (!promote_weight) {
+    TORCH_CHECK(self.dtype() == weight.dtype(), "expected dtype ", self.dtype(),
+                " for `weight` but got dtype ", weight.dtype());
+  }
   build(at::TensorIteratorConfig()
         .allow_cpu_scalars(true)
+        .promote_inputs_to_common_dtype(promote_weight)
+        .enforce_safe_casting_to_output(promote_weight)
+        .cast_common_dtype_to_outputs(promote_weight)
         .add_output(maybe_get_output())
         .add_const_input(self)
         .add_const_input(end)
diff --git a/test/test_binary_ufuncs.py b/test/test_binary_ufuncs.py
index 0035ab30ad0..bdc0d7329df 100644
--- a/test/test_binary_ufuncs.py
+++ b/test/test_binary_ufuncs.py
@@ -3519,6 +3519,24 @@ class TestBinaryUfuncs(TestCase):
                 expected = torch.lerp(xref, yref, wref).to(dtype)
                 self.assertEqual(actual, expected, atol=0.0, rtol=0.0)
 
+    @dtypes(torch.float, torch.double, torch.cfloat, torch.cdouble)
+    def test_lerp_weight_scalar_tensor_promotion(self, device, dtype):
+        start = make_tensor((5, 5), dtype=dtype, device=device, low=1, high=100)
+        end = make_tensor((5, 5), dtype=dtype, device=device, low=1, high=100)
+        weight = torch.rand((), dtype=torch.float, device=device)
+
+        actual = torch.lerp(start, end, weight)
+        expected = start + weight.to(dtype) * (end - start)
+        self.assertEqual(expected, actual)
+
+    @dtypes(torch.double, torch.cfloat, torch.cdouble)
+    def test_lerp_weight_tensor_promotion_error(self, device, dtype):
+        start = make_tensor((5, 5), dtype=dtype, device=device, low=1, high=100)
+        end = make_tensor((5, 5), dtype=dtype, device=device, low=1, high=100)
+        weight = torch.rand((5, 5), dtype=torch.float, device=device)
+        with self.assertRaisesRegex(RuntimeError, "expected dtype"):
+            torch.lerp(start, end, weight)
+
     def _test_logaddexp(self, device, dtype, base2):
         if base2:
             ref_func = np.logaddexp2
diff --git a/torch/_meta_registrations.py b/torch/_meta_registrations.py
index 173c6c95b2b..15d39fdae27 100644
--- a/torch/_meta_registrations.py
+++ b/torch/_meta_registrations.py
@@ -6972,10 +6972,11 @@ def lerp(start, end, weight):
     )
     args = [start, end]
     if isinstance(weight, TensorLike):
-        torch._check(
-            start.dtype == weight.dtype,
-            lambda: f"expected dtype {start.dtype} for `weight`, but got dtype {weight.dtype}",
-        )
+        if weight.ndim != 0:
+            torch._check(
+                start.dtype == weight.dtype,
+                lambda: f"expected dtype {start.dtype} for `weight`, but got dtype {weight.dtype}",
+            )
         args.append(weight)
     return elementwise_meta(
         *args, type_promotion=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT