fix comments

python/tvm/relay/op/reduce.py

@@ -39,7 +39,7 @@ def argmax(data, axis=None, keepdims=False, exclude=False):
 
     exclude : bool
         If `exclude` is true, reduction will be performed on the axes that are
-      NOT in axis instead.
+        NOT in axis instead.
 
     Returns
     -------
@@ -69,7 +69,7 @@ def argmin(data, axis=None, keepdims=False, exclude=False):
 
     exclude : bool
         If `exclude` is true, reduction will be performed on the axes that are
-      NOT in axis instead.
+        NOT in axis instead.
 
     Returns
     -------
@@ -100,7 +100,7 @@ def sum(data, axis=None, keepdims=False, exclude=False):
 
     exclude : bool
         If `exclude` is true, reduction will be performed on the axes that are
-      NOT in axis instead.
+        NOT in axis instead.
 
     Returns
     -------
@@ -112,12 +112,12 @@ def sum(data, axis=None, keepdims=False, exclude=False):
 
 
 def all(data, axis=None, keepdims=False, exclude=False):
-    """Computes the logical and of array elements over given axes.
+    """Computes the logical AND of boolean array elements over given axes.
 
     Parameters
     ----------
     data : relay.Expr
-        The input data
+        The input boolean tensor
 
     axis : None or int or tuple of int
         Axis or axes along which a sum is performed. The default, axis=None,
@@ -131,12 +131,33 @@ def all(data, axis=None, keepdims=False, exclude=False):
 
     exclude : bool
         If `exclude` is true, reduction will be performed on the axes that are
-      NOT in axis instead.
+        NOT in axis instead.
 
     Returns
     -------
     result : relay.Expr
         The computed result.
+
+    Examples
+    --------
+    .. code-block:: python
+
+    data = relay.Constant(tvm.nd.array([[[ True,  True,  True],
+                                         [ True,  True,  True],
+                                         [False,  True, False]],
+                                        [[ True, False, False],
+                                         [ True,  True, False],
+                                         [False,  True,  True]]]))
+
+    relay.all(data, axis=1)
+    # [[False,  True, False],
+    # [False, False, False]]
+
+    relay.all(data, axis=0)
+    # [[ True, False, False],
+    # [ True,  True, False],
+    # [False,  True, False]]
+
     """
     axis = [axis] if axis and isinstance(axis, int) else axis
     return _make.all(data, axis, keepdims, exclude)
@@ -162,7 +183,7 @@ def max(data, axis=None, keepdims=False, exclude=False):
 
     exclude : bool
         If `exclude` is true, reduction will be performed on the axes that are
-      NOT in axis instead.
+        NOT in axis instead.
 
     Returns
     -------
@@ -194,7 +215,7 @@ def min(data, axis=None, keepdims=False, exclude=False):
 
     exclude : bool
         If `exclude` is true, reduction will be performed on the axes that are
-      NOT in axis instead.
+        NOT in axis instead.
 
     Returns
     -------
@@ -225,7 +246,7 @@ def mean(data, axis=None, keepdims=False, exclude=False):
 
     exclude : bool
         If `exclude` is true, reduction will be performed on the axes that are
-      NOT in axis instead.
+        NOT in axis instead.
 
     Returns
     -------
@@ -256,7 +277,7 @@ def prod(data, axis=None, keepdims=False, exclude=False):
 
     exclude : bool
         If `exclude` is true, reduction will be performed on the axes that are
-      NOT in axis instead.
+        NOT in axis instead.
 
     Returns
     -------

src/relay/op/tensor/reduce.cc

@@ -364,7 +364,7 @@ Array<Tensor> AllCompute(const Attrs& attrs,
 
 
 RELAY_REGISTER_REDUCE_OP("all")
-.describe(R"code(Computes the logical AND of array elements over given axes.
+.describe(R"code(Computes the logical AND of boolean array elements over given axes.
 
 Example::
 

tests/python/relay/test_op_level10.py

@@ -249,6 +249,7 @@ def test_adaptive_pool2d():
     verify_adaptive_pool2d((1, 14, 56, 78), (34, 13), "max")
     verify_adaptive_pool2d((1, 5, 46, 97), (4, 96), "avg")
 
+
 if __name__ == "__main__":
     test_adaptive_pool2d()
     test_collapse_sum_like()

tests/python/relay/test_op_level4.py

@@ -138,6 +138,7 @@ def test_where():
 def verify_reduce(funcs, data, axis, keepdims, exclude, output, dtype="float32"):
     test_func = funcs[0]
     ref_func = funcs[1]
+    dtype = "bool" if ref_func in [np.all] else dtype
 
     x = relay.var("x", relay.TensorType(data, dtype))
     z = test_func(x, axis, keepdims, exclude)
@@ -155,7 +156,9 @@ def verify_reduce(funcs, data, axis, keepdims, exclude, output, dtype="float32")
         return
 
     func = relay.Function([x], z)
-    x_data = np.random.uniform(size=data).astype(dtype)
+    x_data = np.random.choice([True, False], size=data) if ref_func in [np.all] \
+        else np.random.uniform(size=data).astype(dtype)
+
     if ref_func in [np.sum]:
         ref_res = ref_func(x_data + 0, axis=axis, dtype=dtype, keepdims=keepdims)
     elif ref_func in [np.max, np.min, np.mean, np.prod]:
@@ -194,6 +197,7 @@ def _wrapper(data, axis=None, keepdims=False):
                  [relay.min, np.min],
                  [relay.mean, np.mean],
                  [relay.prod, np.prod],
+                 [relay.all, np.all],
                  [relay.argmin, _with_keepdims(np.argmin)],
                  [relay.argmax, _with_keepdims(np.argmax)]]:
         verify_reduce(func, (d1, d2, d3, d4), None, False, False, ())
@@ -203,6 +207,7 @@ def _wrapper(data, axis=None, keepdims=False):
         verify_reduce(func, (d1, d2, d3), (0, 1), True, False, (1, 1, d3))
         verify_reduce(func, (2, 3, 4), 1, True, False, (2, 1, 4))
         verify_reduce(func, (2, 3, 4), (1,), True, False, (2, 1, 4))
+        verify_reduce(func, (2, 3, 4), -1, True, False, (2, 3, 1))
         verify_reduce(func, (2, 3, 4), (0, 1, 2), False, False, ())
         verify_reduce(func, (4, 4, 3), None, False, False, ())
         verify_reduce(func, (4, 4, 3), (0, 2), False, False, (4,))

topi/include/topi/reduction.h

@@ -372,9 +372,9 @@ inline Tensor collapse_sum(const Tensor& data, Array<Expr> target_shape) {
 * \brief Creates an operation that computes the logical AND of elements
 * over a given axis
 *
-* \param data The input tensor
-* \param axis The axis to perform logical AND over. If axis is empty, the
-* operation will perform logical AND over all elements of the array.
+* \param data The input boolean tensor
+* \param axis The axes to reduce. If axis is empty, the operation will
+* perform logical AND over all elements of the array.
 * \param keepdims If this is set to true, the axes which are reduced are
 * left in the result as dimensions with size one. This enables the result
 * to broadcast correctly against the input array.

topi/python/topi/reduction.py

@@ -71,7 +71,7 @@ def all(data, axis=None, keepdims=False):
     Parameters
     ----------
     data : tvm.Tensor
-        The input tvm tensor
+        The input tvm boolean tensor
 
     axis : None or int or tuple of int
         Axis or axes along which a logical AND is performed.