fix comments

docs/langref/relay_op.rst

@@ -276,6 +276,7 @@ Level 4 Definitions
 .. autofunction:: tvm.relay.greater_equal
 .. autofunction:: tvm.relay.less
 .. autofunction:: tvm.relay.less_equal
+.. autofunction:: tvm.relay.all
 .. autofunction:: tvm.relay.logical_and
 .. autofunction:: tvm.relay.logical_or
 .. autofunction:: tvm.relay.logical_not
@@ -310,7 +311,6 @@ Level 6 Definitions
 
 Level 10 Definitions
 --------------------
-.. autofunction:: tvm.relay.all
 .. autofunction:: tvm.relay.broadcast_to_like
 .. autofunction:: tvm.relay.collapse_sum_like
 .. autofunction:: tvm.relay.slice_like

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

@@ -362,7 +362,7 @@ 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

@@ -208,34 +208,10 @@ def test_shape_of():
             tvm.testing.assert_allclose(op_res.asnumpy(),
                                         np.array(shape).astype('int32'))
 
-def test_contrib_all():
-    def verify_contrib_all(shape, axis=None, keepdims=False, exclude=False):
-        x = relay.var("x", relay.TensorType(shape, "bool"))
-        z = relay.all(x, axis, keepdims, exclude)
-        zz = relay.ir_pass.infer_type(z)
-        if axis:
-            assert "axis=" in z.astext()
-        if keepdims:
-            assert "keepdims=" in z.astext()
-        if exclude:
-            assert "exclude=" in z.astext()
-
-        func = relay.Function([x], z)
-        x_data = np.random.choice([True, False], size=shape)
-        ref_res = np.all(x_data, axis=axis, keepdims=keepdims)
-        for target, ctx in ctx_list():
-            for kind in ["graph", "debug"]:
-                intrp = relay.create_executor(kind, ctx=ctx, target=target)
-                op_res = intrp.evaluate(func)(x_data)
-                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
-    verify_contrib_all((2, 3, 4), axis=(0,))
-    verify_contrib_all((2, 3, 4, 5, 6), axis=(2, 3), keepdims=True)
-
 if __name__ == "__main__":
     test_collapse_sum_like()
     test_broadcast_to_like()
     test_slice_like()
     test_reverse_reshape()
     test_batch_matmul()
     test_shape_of()
-    test_contrib_all()

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.