diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index a575951f03..92d646e7cf 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -186,6 +186,9 @@ class Tensor(MathTrait):
     lhs,rhs = self._broadcasted(x, reverse)
     return lhs._apply_uop(fxn, rhs)
 
+  # _binop is used by MathTrait
+  def _binop(self, op, x, reverse): return self._apply_broadcasted_uop(lambda *u: UOp.alu(u[0], op, *u[1:]), x, reverse)
+
   def requires_grad_(self, requires_grad=True) -> Tensor:
     self.requires_grad = requires_grad
     return self
@@ -3490,26 +3493,6 @@ class Tensor(MathTrait):
     # broadcast
     return x._broadcast_to(out_shape:=_broadcast_shape(x.shape, y.shape)), y._broadcast_to(out_shape)
 
-  def add(self, x:Tensor|ConstType, reverse=False) -> Tensor:
-    """
-    Adds `self` and `x`.
-    Equivalent to `self + x`.
-    Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.
-
-    ```python exec="true" source="above" session="tensor" result="python"
-    Tensor.manual_seed(42)
-    t = Tensor.randn(4)
-    print(t.numpy())
-    ```
-    ```python exec="true" source="above" session="tensor" result="python"
-    print(t.add(20).numpy())
-    ```
-    ```python exec="true" source="above" session="tensor" result="python"
-    print(t.add(Tensor([[2.0], [3.5]])).numpy())
-    ```
-    """
-    return self._apply_broadcasted_uop(UOp.add, x, reverse)
-
   def sub(self, x:Tensor|ConstType, reverse=False) -> Tensor:
     """
     Subtracts `x` from `self`.
@@ -3531,26 +3514,6 @@ class Tensor(MathTrait):
     a, b = self._broadcasted(x, reverse)
     return a + (-b)
 
-  def mul(self, x:Tensor|ConstType, reverse=False) -> Tensor:
-    """
-    Multiplies `self` and `x`.
-    Equivalent to `self * x`.
-    Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.
-
-    ```python exec="true" source="above" session="tensor" result="python"
-    Tensor.manual_seed(42)
-    t = Tensor.randn(4)
-    print(t.numpy())
-    ```
-    ```python exec="true" source="above" session="tensor" result="python"
-    print(t.mul(3).numpy())
-    ```
-    ```python exec="true" source="above" session="tensor" result="python"
-    print(t.mul(Tensor([[-1.0], [2.0]])).numpy())
-    ```
-    """
-    return self._apply_broadcasted_uop(UOp.mul, x, reverse)
-
   def idiv(self, x:Tensor|ConstType, reverse=False) -> Tensor:
     """
     Divides `self` by `x`.
diff --git a/tinygrad/uop/mathtraits.py b/tinygrad/uop/mathtraits.py
index 81e4d64111..adc2c9d8fb 100644
--- a/tinygrad/uop/mathtraits.py
+++ b/tinygrad/uop/mathtraits.py
@@ -14,8 +14,43 @@ class MathTrait:
   def neg(self):
     if (dtype:=getattr(self, 'dtype')) is None: raise TypeError(f"MathTraits __neg__ requires a dtype, {self=}")
     return self.logical_not() if dtype.scalar() == dtypes.bool else self*(-1)
-  def add(self, x, reverse=False): return self._binop(Ops.ADD, x, reverse)
-  def mul(self, x, reverse=False): return self._binop(Ops.MUL, x, reverse)
+  def add(self, x, reverse=False):
+    """
+    Adds `self` and `x`.
+    Equivalent to `self + x`.
+    Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.
+    ```python exec="true" source="above" session="tensor" result="python"
+    Tensor.manual_seed(42)
+    t = Tensor.randn(4)
+    print(t.numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(t.add(20).numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(t.add(Tensor([[2.0], [3.5]])).numpy())
+    ```
+    """
+    return self._binop(Ops.ADD, x, reverse)
+  def mul(self, x, reverse=False):
+    """
+    Multiplies `self` and `x`.
+    Equivalent to `self * x`.
+    Supports broadcasting to a common shape, type promotion, and integer, float, boolean inputs.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    Tensor.manual_seed(42)
+    t = Tensor.randn(4)
+    print(t.numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(t.mul(3).numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(t.mul(Tensor([[-1.0], [2.0]])).numpy())
+    ```
+    """
+    return self._binop(Ops.MUL, x, reverse)
   def bitwise_and(self, x, reverse=False): return self._binop(Ops.AND, x, reverse)
   def bitwise_or(self, x, reverse=False): return self._binop(Ops.OR, x, reverse)
   def bitwise_xor(self, x, reverse=False): return self._binop(Ops.XOR, x, reverse)