[FRONTEND] fix checks for valid slice and avoid hitting an obscure exception. (#1720)

When comparing to the expected slides, using the `==` operator will
dispatch to the component of the slice. If the user writes `a[10:20]`
these are `triton.constexpr` instances, and the `__eq__` operator which
is implemented as: `return constexpr(self.value == other.value)`. At
this point the access to `.value` on the provided `None` yields an
exception that isn't very friendly to the user.

I am not sure if the implementation of `constexpr` should be hardened
instead?

Co-authored-by: Philippe Tillet <phil@openai.com>

python/test/unit/language/test_core.py

@@ -467,6 +467,21 @@ def test_broadcast(dtype):
     broadcast_kernel[(1,)](x_tri, y_tri, y_broadcasted_tri, M=M, N=N)
     assert (y_broadcasted_np == to_numpy(y_broadcasted_tri)).all()
 
+# ------------------
+# test invalid slice
+# ------------------
+
+
+def test_invalid_slice():
+    dst = torch.empty(128, device='cuda')
+
+    @triton.jit
+    def _kernel(dst):
+        dst[10:]
+
+    with pytest.raises(triton.CompilationError, match='unsupported tensor index'):
+        _kernel[(1,)](dst=dst)
+
 
 # ----------------
 # test expand_dims

python/triton/language/core.py

@@ -711,7 +711,7 @@ class tensor:
         for dim, sl in enumerate(slices):
             if isinstance(sl, constexpr) and sl.value is None:
                 ret = semantic.expand_dims(ret, dim, _builder)
-            elif sl == slice(None, None, None):
+            elif isinstance(sl, slice) and sl.start is None and sl.stop is None and sl.step is None:
                 pass
             else:
                 assert False, f"unsupported tensor index: {sl}"