support interface 'all' and 'any' of tensor

mindspore/_extends/parse/standard_method.py

@@ -31,6 +31,41 @@ trans = P.Transpose()
 shape_ = P.Shape()
 dtype_ = P.DType()
 
+
+def all_(x, axis=(), keep_dims=False):
+    """
+    Check all array elements along a given axis evaluate to True.
+
+    Args:
+        x (Tensor): A Tensor to be reduced.
+        axis (Union[None, int, tuple(int)): Dimensions of reduction.
+        keep_dims (bool): Whether to keep the reduced dimensions.
+
+    Returns:
+        Tensor, has the same data type as x.
+    """
+
+    reduce_all = P.ReduceAll(keep_dims)
+    return reduce_all(x, axis)
+
+
+def any_(x, axis=(), keep_dims=False):
+    """
+    Check any array element along a given axis evaluate to True.
+
+    Args:
+        x (Tensor): A Tensor to be reduced.
+        axis (Union[None, int, tuple(int)): Dimensions of reduction.
+        keep_dims (bool): Whether to keep the reduced dimensions.
+
+    Returns:
+        Tensor, has the same data type as x.
+    """
+
+    reduce_any = P.ReduceAny(keep_dims)
+    return reduce_any(x, axis)
+
+
 def transpose(x):
     """Implementation of `transpose`."""
     shape = F.shape(x)
@@ -157,7 +192,6 @@ def check_is_const_int(x, op_name, arg_name):
     return True
 
 
-
 @constexpr
 def check_is_tensor_bool_cond(shp):
     """check if tensor is a bool condition"""
@@ -316,4 +350,5 @@ def to_array(x):
     """Implementation of `to_array`."""
     return x.__ms_to_array__()
 
+
 tensor_operator_registry.register('__bool__', tensor_bool)

mindspore/ccsrc/pipeline/jit/resource.cc

@@ -143,6 +143,8 @@ BuiltInTypeMap &GetMethodMap() {
      }},
     {kObjectTypeTensorType,
      {
+       {"all", std::string("all_")},                // C.reduce_all
+       {"any", std::string("any_")},                // C.reduce_any
        {"__add__", std::string("add")},             // C.add
        {"__sub__", std::string("sub")},             // C.sub
        {"__mul__", std::string("mul")},             // C.mul

mindspore/common/_register_for_tensor.py

@@ -35,9 +35,11 @@ class Registry(UserDict):
                 new_args = list(args)
                 new_args.append(obj_str)
                 return self["vm_compare"](*new_args)
+
             obj = wrap
         else:
             obj = self[obj_str]
         return obj
 
+
 tensor_operator_registry = Registry()

mindspore/common/tensor.py

@@ -27,7 +27,6 @@ np_types = (np.int8, np.int16, np.int32, np.int64,
             np.float32, np.float64, np.bool_)
 
 
-
 class Tensor(Tensor_):
     """
     Tensor for data storage.
@@ -205,6 +204,34 @@ class Tensor(Tensor_):
             return "Unknown Tensor type!"
         return str(self.asnumpy())
 
+    def all(self, axis=(), keep_dims=False):
+        """
+        Check all array elements along a given axis evaluate to True.
+
+        Args:
+            axis (Union[None, int, tuple(int)): Dimensions of reduction.
+            keep_dims (bool): Whether to keep the reduced dimensions.
+
+        Returns:
+            Tensor, has the same data type as x.
+        """
+
+        return tensor_operator_registry.get('all')(keep_dims)(self, axis)
+
+    def any(self, axis=(), keep_dims=False):
+        """
+        Check any array element along a given axis evaluate to True.
+
+        Args:
+            axis (Union[None, int, tuple(int)): Dimensions of reduction.
+            keep_dims (bool): Whether to keep the reduced dimensions.
+
+        Returns:
+            Tensor, has the same data type as x.
+        """
+
+        return tensor_operator_registry.get('any')(keep_dims)(self, axis)
+
     @property
     def virtual_flag(self):
         """Mark tensor is virtual."""
@@ -257,6 +284,7 @@ class IndexedSlices:
         >>> values = Tensor([[1, 2]], dtype=ms.float32)
         >>> Net((3, 2))(indices, values)
     """
+
     def __init__(self, indices, values, dense_shape):
         raise NotImplementedError
 
@@ -297,5 +325,6 @@ class SparseTensor:
         >>> values = Tensor([1, 2], dtype=ms.float32)
         >>> Net((3, 4))(indices, values)
     """
+
     def __init__(self, indices, values, dense_shape):
         raise NotImplementedError

mindspore/ops/functional.py

@@ -30,7 +30,6 @@ dtype = P.DType()
 isconstant = Primitive('is_constant')
 isconstant.add_prim_attr('const_value', True)
 
-
 issubclass_ = P.IsSubClass()
 isinstance_ = P.IsInstance()
 fill = P.Fill()
@@ -67,6 +66,7 @@ assign_sub = P.AssignSub()
 assign = P.Assign()
 square = P.Square()
 sqrt = P.Sqrt()
+
 scalar_to_array = P.ScalarToArray()
 scalar_to_tensor = P.ScalarToTensor()
 tuple_to_array = P.TupleToArray()
@@ -83,7 +83,6 @@ partial = P.Partial()
 # depend: mount a node to another node
 depend = P.Depend()
 
-
 tuple_setitem = Primitive('tuple_setitem')
 tuple_getitem = Primitive('tuple_getitem')
 list_getitem = Primitive('list_getitem')
@@ -102,7 +101,6 @@ tuple_equal = Primitive("tuple_equal")
 list_equal = Primitive("list_equal")
 make_ref = Primitive("make_ref")
 
-
 scalar_add = Primitive('scalar_add')
 scalar_mul = Primitive('scalar_mul')
 scalar_sub = Primitive('scalar_sub')
@@ -154,7 +152,6 @@ shape_mul = Primitive("shape_mul")
 # a primitive to compare between tuple.
 stop_gradient = Primitive("stop_gradient")
 
-
 make_indexed_slices = Primitive('MakeIndexedSlices')
 indexed_slices_get_values = Primitive('IndexedSlicesGetValues')
 indexed_slices_get_indices = Primitive('IndexedSlicesGetIndices')
@@ -172,7 +169,9 @@ tensor_operator_registry.register('__truediv__', tensor_div)
 tensor_operator_registry.register('__mod__', tensor_mod)
 tensor_operator_registry.register('__pow__', tensor_pow)
 tensor_operator_registry.register('__floordiv__', tensor_floordiv)
-#ms cannot support Tensor(True) compare
+tensor_operator_registry.register('all', P.ReduceAll)
+tensor_operator_registry.register('any', P.ReduceAny)
+# ms cannot support Tensor(True) compare
 tensor_operator_registry.register('__eq__', equal)
 tensor_operator_registry.register('__ne__', not_equal)
 tensor_operator_registry.register('__neg__', neg_tensor)
@@ -181,6 +180,6 @@ tensor_operator_registry.register('__le__', tensor_le)
 tensor_operator_registry.register('__gt__', tensor_gt)
 tensor_operator_registry.register('__ge__', tensor_ge)
 tensor_operator_registry.register('shape', shape)
-#support GE backend for no compare operators
+# support GE backend for no compare operators
 tensor_operator_registry.register('vm_compare', BP.vm_compare)
 tensor_operator_registry.register('cast', cast)

mindspore/ops/operations/math_ops.py

@@ -1111,6 +1111,7 @@ class Mul(_MathBinaryOp):
         >>> mul(input_x, input_y)
         [4, 10, 18]
     """
+
     def infer_value(self, x, y):
         if x is not None and y is not None:
             x = x.asnumpy()

tests/ut/python/pipeline/infer/test_interface_all_and_any_of_tensor.py

@@ -0,0 +1,62 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+""" test interface 'all' and 'any' of tensor """
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+
+
+def test_all_and_any_of_tensor_in_graph():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            all_ = x.all()
+            any_ = x.any()
+            all_0 = x.all(0, True)
+            any_0 = x.any(0, True)
+            return all_, any_, all_0, any_0
+
+    net = Net()
+    x = Tensor(np.array([[True, False, False], [True, False, False]]))
+    context.set_context(mode=context.GRAPH_MODE)
+    net(x)
+
+
+def test_all_and_any_of_tensor_in_pynative():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x):
+            all_ = x.all()
+            any_ = x.any()
+            all_0 = x.all(0, True)
+            any_0 = x.any(0, True)
+            return all_, any_, all_0, any_0
+
+    net = Net()
+    x = Tensor(np.array([[True, False, True], [True, False, False]]))
+    context.set_context(mode=context.PYNATIVE_MODE)
+    ret = net(x)
+    assert ret[0].asnumpy() == np.array(False)
+    assert ret[1].asnumpy() == np.array(True)
+    assert ret[2].asnumpy().shape == np.array([[True, False, False]]).shape
+    assert (ret[2].asnumpy() == np.array([[True, False, False]])).all()
+    assert ret[3].shape == Tensor(np.array([[True, False, True]])).shape
+    assert (ret[3] == Tensor(np.array([[True, False, True]]))).all()

tests/vm_impl/array_ops_vm_impl.py

@@ -194,7 +194,19 @@ def vm_impl_all(self):
 
     def vm_impl(x, axis):
         x = x.asnumpy()
-        out = vm.all(x, axis)
+        out = vm.all(x, axis, self.keep_dims)
+        return Tensor(out)
+
+    return vm_impl
+
+
+@vm_impl_getters.register(P.ReduceAny)
+def vm_impl_any(self):
+    """Generate vm_impl function for Any"""
+
+    def vm_impl(x, axis):
+        x = x.asnumpy()
+        out = vm.any(x, axis, self.keep_dims)
         return Tensor(out)
 
     return vm_impl

tests/vm_impl/vm_interface.py

@@ -67,3 +67,5 @@ setattr(vm, "tanh", tanh)
 setattr(vm, "sigmoid", sigmoid)
 setattr(vm, 'maximum', maximum)
 setattr(vm, 'minimum', minimum)
+setattr(vm, 'all', all_)
+setattr(vm, 'any', any_)

tests/vm_impl/vm_me.py

@@ -840,3 +840,35 @@ def minimum(x, y):
         numpy.ndarray, has the same type as x.
     """
     return np.minimum(x, y)
+
+
+def all_(x, axis=(), keep_dims=False):
+    """
+    Check all array elements along a given axis evaluate to True.
+
+    Args:
+        x (numpy.ndarray): An array to be reduced.
+        axis (Union[None, int, tuple(int)): Dimensions of reduction.
+        keep_dims (bool): Whether to keep the reduced dimensions.
+
+    Returns:
+        numpy.ndarray, has the same type as x.
+    """
+    axis = None if axis == () else axis
+    return np.all(x, axis, keepdims=keep_dims)
+
+
+def any_(x, axis=(), keep_dims=False):
+    """
+    Check any array element along a given axis evaluate to True.
+
+    Args:
+        x (numpy.ndarray): An array to be reduced.
+        axis (Union[None, int, tuple(int)): Dimensions of reduction.
+        keep_dims (bool): Whether to keep the reduced dimensions.
+
+    Returns:
+        numpy.ndarray, has the same type as x.
+    """
+    axis = None if axis == () else axis
+    return np.any(x, axis, keepdims=keep_dims)