update torch.where to throw error for single input

thunder/tests/opinfos.py

@@ -2484,10 +2484,26 @@ def where_sample_generator(op, device, dtype, requires_grad, **kwargs):
         yield SampleInput(pred, a, b)
 
 
+def where_error_generator(op, device, dtype=torch.float32, **kwargs):
+    make = partial(make_tensor, device=device, dtype=dtype)
+    err_msg = r"torch.where\(\) does not support only specifying a condition"
+    yield (
+        SampleInput(
+            make(
+                5,
+            )
+        ),
+        NotImplementedError,
+        err_msg,
+    )
+    yield (SampleInput(make(2, 1, 2)), NotImplementedError, err_msg)
+
+
 where_opinfo = OpInfo(
-    clang.where,
+    ltorch.where,
     supports_grad=True,
     sample_input_generator=where_sample_generator,
+    error_input_generator=where_error_generator,
     torch_reference=torch.where,
 )
 conditional_and_mask_ops.append(where_opinfo)

thunder/tests/test_jit_general.py

@@ -621,7 +621,10 @@ def test_nanogpt():
         "falcon-7b-like",
         "falcon-40b-like",
         "codellama2-like",
-        pytest.param("mixtral-like", marks=pytest.mark.xfail(raises=TypeError, reason="topk", strict=True)),
+        pytest.param(
+            "mixtral-like",
+            marks=pytest.mark.xfail(raises=(NotImplementedError, TypeError), reason="topk and where", strict=True),
+        ),
     ),
 )
 @pytest.mark.parametrize(
@@ -670,7 +673,10 @@ def test_litgpt_variants(name, device):
         "falcon-7b-like",
         "falcon-40b-like",
         "codellama2-like",
-        pytest.param("mixtral-like", marks=pytest.mark.xfail(raises=TypeError, reason="topk", strict=True)),
+        pytest.param(
+            "mixtral-like",
+            marks=pytest.mark.xfail(raises=(NotImplementedError, TypeError), reason="topk and where", strict=True),
+        ),
     ),
 )
 @pytest.mark.parametrize(

thunder/torch/__init__.py

@@ -1565,7 +1565,14 @@ def tril(a: TensorLike, /, diagonal: int = 0, *, fill_value: None | Number = Non
 
 
 @torchsymbol(torch.where, is_method=True)
-def where(pred: TensorLike, a: Number | TensorLike, b: Number | TensorLike, /) -> TensorLike:
+def where(
+    pred: TensorLike, a: None | Number | TensorLike = None, b: None | Number | TensorLike = None, /
+) -> TensorLike:
+    utils.check(
+        isinstance(a, (Number, TensorProxy)) and isinstance(b, (Number, TensorProxy)),
+        lambda: f"torch.where() does not support only specifying a condition",
+        exception_type=NotImplementedError,
+    )
     return clang.where(pred, a, b)