!3324 add reduce_any op for vm

Merge pull request !3324 from fangzehua/reduce_any

mindspore/ccsrc/backend/kernel_compiler/tbe/tbe_adapter.cc

@@ -127,6 +127,7 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"apply_rms_prop", "apply_rms_prop_d"},
   {"cum_prod", "cumprod_d"},
   {"reduce_all", "reduce_all_d"},
+  {"reduce_any", "reduce_any_d"},
   {"sparse_apply_adagrad", "sparse_apply_adagrad_d"},
   {"unsorted_segment_min", "unsorted_segment_min_d"},
   {"reduce_prod", "reduce_prod_d"},

mindspore/ccsrc/backend/optimizer/pass/const_input_to_attr_registry.cc

@@ -46,6 +46,7 @@ ConstInputToAttrInfoRegistry::ConstInputToAttrInfoRegistry() {
   Register(prim::kPrimCumSum->name(), {1});
   Register(prim::kPrimCumProd->name(), {1});
   Register(prim::kPrimReduceAll->name(), {1});
+  Register(prim::kPrimReduceAny->name(), {1});
   Register(prim::kPrimUnsortedSegmentMin->name(), {2});
   Register(kSparseGatherV2, {2});
   Register(kUnsortedSegmentProdOpName, {2});

mindspore/core/base/core_ops.h

@@ -34,6 +34,7 @@ inline const PrimitivePtr kPrimMinimumGrad = std::make_shared<Primitive>("Minimu
 inline const PrimitivePtr kPrimReduceMean = std::make_shared<Primitive>("ReduceMean");
 inline const PrimitivePtr kPrimReduceSum = std::make_shared<Primitive>("ReduceSum");
 inline const PrimitivePtr kPrimReduceAll = std::make_shared<Primitive>("ReduceAll");
+inline const PrimitivePtr kPrimReduceAny = std::make_shared<Primitive>("ReduceAny");
 inline const PrimitivePtr kPrimReduceMax = std::make_shared<Primitive>("ReduceMax");
 inline const PrimitivePtr kPrimReduceMin = std::make_shared<Primitive>("ReduceMin");
 inline const PrimitivePtr kPrimNeg = std::make_shared<Primitive>("Neg");

mindspore/ops/_grad/grad_math_ops.py

@@ -641,6 +641,16 @@ def get_bprop_reduceall(self):
     return bprop
 
 
+@bprop_getters.register(P.ReduceAny)
+def get_bprop_reduceany(self):
+    """Grad definition for `ReduceAny` operation."""
+
+    def bprop(x, axis, out, dout):
+        return zeros_like(x), zeros_like(axis)
+
+    return bprop
+
+
 @bprop_getters.register(P.ReduceMax)
 def get_bprop_reducemax(self):
     """Grad definition for `Max` operation."""

mindspore/ops/_op_impl/tbe/__init__.py

@@ -246,6 +246,7 @@ from .bitwise_and import _bitwise_and_tbe
 from .bitwise_or import _bitwise_or_tbe
 from .bitwise_xor import _bitwise_xor_tbe
 from .reduce_all import _reduce_all_tbe
+from .reduce_any import _reduce_any_tbe
 from .sparse_apply_adagrad import _sparse_apply_adagrad_tbe
 from .unsorted_segment_min import _unsorted_segment_min_tbe
 from .asin import _asin_tbe

mindspore/ops/_op_impl/tbe/reduce_any.py

@@ -0,0 +1,38 @@
+# 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.
+# ============================================================================
+
+"""ReduceAny op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+reduce_any_op_info = TBERegOp("ReduceAny") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("reduce_any_d.so") \
+    .compute_cost(10) \
+    .kernel_name("reduce_any_d") \
+    .partial_flag(True) \
+    .attr("axis", "required", "listInt", "all") \
+    .attr("keep_dims", "optional", "bool", "all") \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .op_pattern("reduce") \
+    .dtype_format(DataType.BOOL_None, DataType.BOOL_None) \
+    .get_op_info()
+
+
+@op_info_register(reduce_any_op_info)
+def _reduce_any_tbe():
+    """ReduceAny TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -44,7 +44,7 @@ from .inner_ops import ScalarCast
 
 from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, AssignSub, Atan2, BatchMatMul, BitwiseAnd, BitwiseOr,
                        BitwiseXor, Inv, Invert, ApproximateEqual, InplaceAdd, InplaceSub,
-                       ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd,
+                       ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd, ReduceAny,
                        Cos, Div, DivNoNan, Equal, EqualCount, Exp, Expm1, Erf, Erfc, Floor, FloorDiv, FloorMod, Ceil,
                        Acosh, Greater, GreaterEqual, Less, LessEqual, Log, Log1p, LogicalAnd, Mod,
                        LogicalNot, LogicalOr, MatMul, Maximum,
@@ -215,6 +215,7 @@ __all__ = [
     'CTCLoss',
     'RNNTLoss',
     'ReduceAll',
+    'ReduceAny',
     'ScalarToArray',
     'ScalarToTensor',
     'TupleToArray',

mindspore/ops/operations/math_ops.py

@@ -405,6 +405,42 @@ class ReduceAll(_Reduce):
         return self.do_infer(input_x, axis, (mstype.bool_,))
 
 
+class ReduceAny(_Reduce):
+    """
+    Reduce a dimension of a tensor by the "logical or" of all elements in the dimension.
+
+    The dtype of the tensor to be reduced is bool.
+
+    Args:
+       keep_dims (bool): If True, keep these reduced dimensions and the length is 1.
+                         If False, don't keep these dimensions.
+                         Default : False, don't keep these reduced dimensions.
+
+    Inputs:
+        - **input_x** (Tensor[bool]) - The input tensor.
+        - **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions.
+          Only constant value is allowed.
+
+    Outputs:
+        Tensor, the dtype is bool.
+
+        - If axis is (), and keep_dims is false,
+          the output is a 0-D tensor representing the "logical or" of of all elements in the input tensor.
+        - If axis is int, set as 2, and keep_dims is false,
+          and keep_dims is false, the shape of output is :math:`(x_1, x_3, ..., x_R)`.
+        - If axis is tuple(int), set as (2, 3), and keep_dims is false,
+          the shape of output is :math:`(x_1, x_4, ..., x_R)`.
+
+    Examples:
+        >>> input_x = Tensor(np.array([[True, False], [True, True]]))
+        >>> op = P.ReduceAny(keep_dims=True)
+        >>> output = op(input_x, 1)
+    """
+
+    def __infer__(self, input_x, axis):
+        return self.do_infer(input_x, axis, (mstype.bool_,))
+
+
 class ReduceMax(_Reduce):
     """
     Reduce a dimension of a tensor by the maximum value in this dimension.

tests/ut/python/ops/test_ops.py

@@ -1186,6 +1186,11 @@ test_case_math_ops = [
         'desc_const': [1],
         'desc_inputs': [Tensor(np.array([[True, False], [True, True]]))],
         'desc_bprop': []}),
+    ('ReduceAny', {
+        'block': P.ReduceAny(),
+        'desc_const': [1],
+        'desc_inputs': [Tensor(np.array([[True, False], [True, True]]))],
+        'desc_bprop': []}),
     ('BesselI0e', {
         'block': P.BesselI0e(),
         'desc_inputs': [[2, 3]],