fix(mge): fix bool.sum()

GitOrigin-RevId: 62c482db40

imperative/python/megengine/core/tensor/tensor_wrapper.py

@@ -158,6 +158,10 @@ def _reduce(mode):
     def f(self, axis=None, keepdims: bool = False):
         data = self
         (data,) = utils.convert_inputs(data)
+        if mode == "MEAN":
+            data = data.astype("float32")
+        elif self.dtype == np.bool_:
+            data = data.astype("int32")
         if axis is None:
             data = data.reshape(-1)
             assert not keepdims, "can not set axis=None and keepdims=True"
@@ -180,6 +184,9 @@ def _reduce(mode):
 
             if not keepdims:
                 result = _remove_axis(result, axis)
+        if self.dtype == np.bool_:
+            if mode in ["MIN", "MAX"]:
+                result = result.astype("bool")
         return result
 
     return f
@@ -377,7 +384,38 @@ class ArrayMethodMixin(abc.ABC):
     def flatten(self):
         return self.reshape(-1)
 
-    sum = _reduce("SUM")
+    def sum(self, axis=None, keepdims: bool = False):
+        r"""Returns the sum of each row of the input tensor in the given dimension ``axis``.
+        If ``axis`` is a list of axises, reduce over all of them.
+        
+        If ``keepdims`` is ``True``, the shape of output tensor is the same as the input tensor, except in the dimension(s) ``axis`` where it is of size 1. Otherwise, ``axis`` is squeezed(see :meth:`~.functional.tensor.remove_axis`).
+
+        Same for prod/mean/max/min.
+
+        :param axis: the dimension or dimensions to reduce.
+        :param keepdim: whether the output tensor has ndim retained or not.
+        :return: output tensor.
+
+        Examples:
+
+        .. testcode::
+
+            from megengine import tensor
+            a = tensor([False, True, True, False])
+            b = tensor([1.0, 2.0, 3.0, 4.0])
+            print(a.sum().numpy())
+            print(b.sum().numpy())
+
+        Outputs:
+
+        .. testoutput::
+
+            [2]
+            [10.]
+
+        """
+        return _reduce("SUM")(self, axis, keepdims)
+
     prod = _reduce("PRODUCT")
     min = _reduce("MIN")
     max = _reduce("MAX")

imperative/python/test/unit/core/test_tensor_wrapper.py

@@ -35,11 +35,16 @@ def test_matmul():
 
 
 def test_reduce():
-    for m in ["sum", "prod", "min", "max", "mean"]:
-        x_np = np.random.rand(10).astype("float32")
-        x = TensorWrapper(x_np)
-        y = getattr(x, m)(axis=-1, keepdims=True)
-        np.testing.assert_almost_equal(y.numpy(), getattr(x_np, m)(-1), decimal=6)
+    def test_x(x_np):
+        for m in ["sum", "prod", "min", "max", "mean"]:
+            x = TensorWrapper(x_np)
+            y = getattr(x, m)(axis=-1, keepdims=True)
+            np.testing.assert_almost_equal(y.numpy(), getattr(x_np, m)(-1), decimal=6)
+
+    test_x((10 * np.random.rand(10) + 1).astype("int32"))
+    test_x(np.random.rand(10).astype("float32"))
+    test_x(np.array([True, True, True]))
+    test_x(np.array([True, False, True]))
 
 
 def test_set_subtensor():