!4265 support two mstypes do equal.

Merge pull request !4265 from zhangbuxue/support_two_mstypes_do_equal

mindspore/ccsrc/frontend/operator/ops_front_infer_function.cc

@@ -206,7 +206,7 @@ bool CompareShape(const std::vector<ValuePtr> &x_shape, const std::vector<ValueP
 }
 
 AbstractBasePtr DoInferReduceShape(const AbstractTuplePtr &x_shape, const ValuePtr &x_shp_value,
-                                   const ValueTuplePtr &axis_value_ptr, const PrimitivePtr &primitive) {
+                                   const ValueSequeuePtr &axis_value_ptr, const PrimitivePtr &primitive) {
   size_t x_rank = x_shape->size();
   std::set<int> axis_set;
   auto axis_data = axis_value_ptr->value();
@@ -348,17 +348,17 @@ AbstractBasePtr InferImplReduceShape(const AnalysisEnginePtr &, const PrimitiveP
                       << " evaluator shape's data field can't be anything: " << args_spec_list[1]->ToString();
   }
 
-  // Axis can be scalar, tuple or None
-  AbstractTuplePtr axis = nullptr;
+  // Axis can be scalar, tuple or list
+  AbstractSequeuePtr axis = nullptr;
   if (args_spec_list[1]->isa<AbstractScalar>()) {
     MS_LOG(DEBUG) << op_name << " evaluator second parameter is scalar";
     AbstractBasePtrList axis_list = {dyn_cast<AbstractScalar>(args_spec_list[1])};
     axis = std::make_shared<AbstractTuple>(axis_list);
-  } else if (args_spec_list[1]->isa<AbstractTuple>()) {
-    MS_LOG(DEBUG) << op_name << " evaluator second parameter is tuple";
-    axis = args_spec_list[1]->cast<AbstractTuplePtr>();
+  } else if (args_spec_list[1]->isa<AbstractSequeue>()) {
+    MS_LOG(DEBUG) << op_name << " evaluator second parameter is sequeue";
+    axis = args_spec_list[1]->cast<AbstractSequeuePtr>();
   } else {
-    MS_LOG(EXCEPTION) << op_name << " evaluator second parameter should be a scalar or tuple, but got "
+    MS_LOG(EXCEPTION) << op_name << " evaluator second parameter should be a scalar or tuple or list, but got "
                       << args_spec_list[1]->ToString();
   }
 
@@ -367,7 +367,7 @@ AbstractBasePtr InferImplReduceShape(const AnalysisEnginePtr &, const PrimitiveP
     MS_LOG(EXCEPTION) << op_name
                       << " evaluator shape's data field can't be anything: " << args_spec_list[1]->ToString();
   }
-  auto axis_value_ptr = axis_value->cast<ValueTuplePtr>();
+  auto axis_value_ptr = axis_value->cast<ValueSequeuePtr>();
   MS_EXCEPTION_IF_NULL(axis_value_ptr);
 
   return DoInferReduceShape(shape_x, x_shp_value, axis_value_ptr, primitive);

mindspore/ccsrc/pipeline/pynative/pynative_execute.cc

@@ -261,17 +261,19 @@ void ConvertInputs(const PrimitivePyPtr &prim, const py::list &args, const OpExe
     auto obj = out_args[i];
     if (py::isinstance<tensor::Tensor>(obj)) {
       auto arg = py::cast<tensor::TensorPtr>(obj);
-      if (arg->data_type() == it->second) {
+      TypeId arg_type_id = arg->data_type();
+      if (prim::type_map.find(arg_type_id) == prim::type_map.end() || arg_type_id == it->second) {
         continue;
       }
       if (signature[i].rw == SignatureEnumRW::kRWWrite) {
-        prim::RaiseExceptionForConvertRefDtype(prim->name(), TypeIdToMsTypeStr(arg->data_type()),
+        prim::RaiseExceptionForConvertRefDtype(prim->name(), TypeIdToMsTypeStr(arg_type_id),
                                                TypeIdToMsTypeStr(it->second));
       }
     }
 
     if (!py::isinstance<tensor::Tensor>(obj) && !py::isinstance<py::int_>(obj) && !py::isinstance<py::float_>(obj)) {
-      MS_EXCEPTION(TypeError) << "For '" << prim->name() << "', the " << i << "th input is a not support type: "
+      MS_EXCEPTION(TypeError) << "For '" << prim->name() << "', the " << i
+                              << "th input is a not support implicit conversion type: "
                               << py::cast<std::string>(obj.attr("__class__").attr("__name__")) << ", and the value is "
                               << py::cast<py::str>(obj) << ".";
     }

mindspore/common/tensor.py

@@ -239,7 +239,8 @@ class Tensor(Tensor_):
         Check all array elements along a given axis evaluate to True.
 
         Args:
-            axis (Union[None, int, tuple(int)): Dimensions of reduction.
+            axis (Union[None, int, tuple(int)): Dimensions of reduction,
+                when axis is None or empty tuple, reduce all dimensions.
                 Default: (), reduce all dimensions.
             keep_dims (bool): Whether to keep the reduced dimensions.
                 Default : False, don't keep these reduced dimensions.
@@ -257,7 +258,8 @@ class Tensor(Tensor_):
         Check any array element along a given axis evaluate to True.
 
         Args:
-            axis (Union[None, int, tuple(int)): Dimensions of reduction.
+            axis (Union[None, int, tuple(int)): Dimensions of reduction,
+                when axis is None or empty tuple, reduce all dimensions.
                 Default: (), reduce all dimensions.
             keep_dims (bool): Whether to keep the reduced dimensions.
                 Default : False, don't keep these reduced dimensions.

mindspore/core/ir/dtype_extends.cc

@@ -338,21 +338,21 @@ TypePtr FunctionStrToType(const std::string &type_name) {
 
 TypePtr StringToType(const std::string &type_name) {
   TypePtr type = nullptr;
-  if (type_name.compare("None") == 0) {
+  if (type_name == "None") {
     type = std::make_shared<TypeNone>();
-  } else if (type_name.compare("Ellipsis") == 0) {
+  } else if (type_name == "Ellipsis") {
     type = std::make_shared<TypeEllipsis>();
-  } else if (type_name.compare("TypeType") == 0) {
+  } else if (type_name == "TypeType") {
     type = std::make_shared<TypeType>();
-  } else if (type_name.compare("SymbolicKeyType") == 0) {
+  } else if (type_name == "SymbolicKeyType") {
     type = std::make_shared<SymbolicKeyType>();
-  } else if (type_name.compare("RefKeyType") == 0) {
+  } else if (type_name == "RefKeyType") {
     type = std::make_shared<RefKeyType>();
-  } else if (type_name.compare("EnvType") == 0) {
+  } else if (type_name == "EnvType") {
     type = std::make_shared<EnvType>();
-  } else if (type_name.compare("Number") == 0) {
+  } else if (type_name == "Number") {
     type = std::make_shared<Number>();
-  } else if (type_name.compare("Bool") == 0) {
+  } else if (type_name == "Bool") {
     type = std::make_shared<Bool>();
   } else if (type_name.compare(0, strlen("Int"), "Int") == 0) {
     type = StringToNumberType<Int>(type_name, "Int");
@@ -372,16 +372,18 @@ TypePtr StringToType(const std::string &type_name) {
     type = ListStrToType(type_name);
   } else if (type_name.compare(0, strlen("Tuple"), "Tuple") == 0) {
     type = TupleStrToType(type_name);
-  } else if (type_name.compare("Slice") == 0) {
+  } else if (type_name == "Slice") {
     type = std::make_shared<Slice>();
-  } else if (type_name.compare("Dictionary") == 0) {
+  } else if (type_name == "Dictionary") {
     type = std::make_shared<Dictionary>();
-  } else if (type_name.compare("String") == 0) {
+  } else if (type_name == "String") {
     type = std::make_shared<String>();
-  } else if (type_name.compare("Problem") == 0) {
+  } else if (type_name == "Problem") {
     type = std::make_shared<Problem>();
   } else if (type_name.compare(0, strlen("Function"), "Function") == 0) {
     type = FunctionStrToType(type_name);
+  } else if (type_name == "mstype") {
+    type = std::make_shared<TypeType>();
   } else {
     // - unsupported to convert
     // Class
@@ -389,7 +391,6 @@ TypePtr StringToType(const std::string &type_name) {
     // JTagged
     // Anything
     // External
-    // Problem
     MS_LOG(EXCEPTION) << "Unsupported type name: " << type_name << "!";
   }
   return type;
@@ -403,10 +404,7 @@ bool IsParentOrChildrenType(TypePtr const &x, TypePtr const &base_type) {
   if (base_type->type_id() == kTypeUnknown || x->type_id() == kTypeUnknown) {
     return false;
   }
-  if (base_type->type_id() == x->parent_type() || x->type_id() == base_type->parent_type()) {
-    return true;
-  }
-  return false;
+  return base_type->type_id() == x->parent_type() || x->type_id() == base_type->parent_type();
 }
 
 bool IsIdentidityOrSubclass(TypePtr const &x, TypePtr const &base_type) {

mindspore/core/ir/func_graph_cloner.cc

@@ -206,8 +206,8 @@ void Cloner::InlineCloneParameters(const FuncGraphPtr &func_graph, const AnfNode
   MS_EXCEPTION_IF_NULL(func_graph);
   auto &old_params = func_graph->parameters();
   if (old_params.size() != params.size()) {
-    MS_LOG(EXCEPTION) << "Origin params size[" << old_params.size() << "], inline params size[" << params.size() << "]";
-    return;
+    MS_EXCEPTION(TypeError) << "Origin params size[" << old_params.size() << "], inline params size[" << params.size()
+                            << "]";
   }
   for (size_t i = 0; i < old_params.size(); ++i) {
     repl_node_[old_params[i]] = params[i];

mindspore/ops/composite/multitype_ops/_constexpr_utils.py

@@ -762,3 +762,10 @@ def get_stride_info_from_tuple(data_shape, index_tuple):
         end_strides.append(data_shape[item])
         step_strides.append(1)
     return tuple(begin_strides), tuple(end_strides), tuple(step_strides), shrink_axis
+
+
+@constexpr
+def mstype_eq(x, y):
+    if x == y:
+        return True
+    return False

mindspore/ops/composite/multitype_ops/equal_impl.py

@@ -14,7 +14,7 @@
 # ============================================================================
 
 """equal_impl"""
-
+from . import _constexpr_utils as const_utils
 from ...composite import base
 from ... import functional as F
 
@@ -32,8 +32,8 @@ def _equal_scalar(x, y):
     Determine if two numbers are equal.
 
     Args:
-       x (Number): x
-       y (NUmber): y
+       x (Number): first input number.
+       y (NUmber): second input number.
 
     Returns:
        bool, if x == y return true, x != y return false.
@@ -41,14 +41,29 @@ def _equal_scalar(x, y):
     return F.scalar_eq(x, y)
 
 
+@equal.register("mstype", "mstype")
+def _equal_mstype(x, y):
+    """
+    Determine if two mindspore types are equal.
+
+    Args:
+       x (mstype): first input mindspore type.
+       y (mstype): second input mindspore type.
+
+    Returns:
+       bool, if x == y return true, x != y return false.
+   """
+    return const_utils.mstype_eq(x, y)
+
+
 @equal.register("String", "String")
 def _equal_string(x, y):
     """
     Determine if two strings are equal.
 
     Args:
-       x: str
-       y: str
+       x (str): first input string.
+       y (str): second input string.
 
     Returns:
        bool, if x == y return true, x != y return false.
@@ -62,8 +77,8 @@ def _string_equal_none(x, y):
     Determine if string equals none.
 
     Args:
-       x: str.
-       y: None.
+       x (str): first input string.
+       y (None) second input None.
 
     Returns:
        bool, return false.
@@ -77,8 +92,8 @@ def _none_equal_string(x, y):
     Determine if string equals none.
 
     Args:
-       x: None.
-       y: str.
+       x (None): first input None.
+       y (str): second input string.
 
     Returns:
        bool, return false.
@@ -92,8 +107,8 @@ def _none_equal_none(x, y):
     Determine if none equals none.
 
     Args:
-       x: None.
-       y: None.
+       x (None): first input None.
+       y (None): second input None.
 
     Returns:
        bool, return true.
@@ -107,8 +122,8 @@ def _scalar_equal_none(x, y):
     Determine if number equals none.
 
     Args:
-       x: Number.
-       y: None.
+       x (Number): first input number.
+       y (None): second input None.
 
     Returns:
        bool, return false.
@@ -122,8 +137,8 @@ def _none_equal_scalar(x, y):
     Determine if number equals none.
 
     Args:
-       x: None.
-       y: NUmber.
+       x (None): first input None.
+       y (Number): second input Number.
 
     Returns:
        bool, return false.
@@ -137,8 +152,8 @@ def _euqal_tuple(x, y):
     Determine if two tuples are equal by element.
 
     Args:
-       x (tuple): x
-       y (tuple): y
+       x (tuple): first input tuple.
+       y (tuple): second input tuple.
 
     Returns:
        bool, if x and y are equal by element return true, else return false.
@@ -152,8 +167,8 @@ def _euqal_list(x, y):
     Determine if two lists are equal by element.
 
     Args:
-       x (list): x
-       y (list): y
+       x (list): first input list.
+       y (list): second input list.
 
     Returns:
        bool, if x and y are equal by element return true, else return false.
@@ -167,8 +182,8 @@ def _tuple_euqal_none(x, y):
     Determine if tuple element equals none element.
 
     Args:
-       x: Tuple.
-       y: None.
+       x(tuple): first input tuple.
+       y (None): second input None.
 
     Returns:
        bool, return false.
@@ -182,8 +197,8 @@ def _none_equal_tuple(x, y):
     Determine if tuple element equals none element.
 
     Args:
-       x: None.
-       y: Tuple.
+       x (None): first input None.
+       y (tuple): second input tuple.
 
     Returns:
        bool, return false.
@@ -199,8 +214,8 @@ def _tensor_equal_tensor(x, y):
     Determine if two tensors are equal.
 
     Args:
-       x : Tensor.
-       y : Tensor.
+       x (Tensor): first input tensor.
+       y (Tensor): second input tensor.
 
     Returns:
        bool, if x == y return true, x != y return false.
@@ -214,8 +229,8 @@ def _tensor_equal_none(x, y):
     Determine if tensor equal none.
 
     Args:
-       x : Tensor.
-       y : None.
+       x (Tensor): first input tensor.
+       y (None): second input None.
 
     Returns:
        bool, return false.
@@ -229,8 +244,8 @@ def _none_equal_tensor(x, y):
     Determine if tensor equal none.
 
     Args:
-       x : None.
-       y : Tensor.
+       x (None): first input None.
+       y (Tensor): second input tensor.
 
     Returns:
        bool, return false.
@@ -245,7 +260,7 @@ def _list_equal_none(x, y):
 
     Args:
        x (list): The first input which is a list.
-       y (none): The second input which is none.
+       y (None): The second input which is none.
 
     Returns:
        bool, return false.
@@ -259,7 +274,7 @@ def _none_equal_list(x, y):
     Determine if none equal list.
 
     Args:
-       x (none): The first input which is none.
+       x (None): The first input which is none.
        y (list): The second input which is a list.
 
     Returns:

tests/st/pynative/test_implicit_conversion.py

@@ -49,6 +49,7 @@ def test_bool_and_int_tensor_add():
     ret_expect = Tensor(np.array([[2, 3, 4], [5, 6, 7]], dtype=np.int32))
     assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
 
+
 def test_float_tensor_and_int_tensor_add():
     x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
     y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))

tests/ut/python/pipeline/parse/test_equal_two_mstype.py

@@ -0,0 +1,65 @@
+# 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 enumerate"""
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+from mindspore.common import dtype as mstype
+
+context.set_context(mode=context.GRAPH_MODE)
+
+
+def test_equal_two_const_mstype():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.type_base = mstype.float32
+            self.type_0 = mstype.float32
+            self.type_1 = mstype.float16
+            self.type_2 = mstype.int32
+            self.type_3 = mstype.tuple_
+
+        def construct(self):
+            ret_0 = self.type_0 == self.type_base
+            ret_1 = self.type_1 == self.type_base
+            ret_2 = self.type_2 == self.type_base
+            ret_3 = self.type_3 == self.type_base
+            return ret_0, ret_1, ret_2, ret_3
+
+    net = Net()
+    assert net() == (True, False, False, False)
+
+
+def test_equal_two_tensor_mstype():
+    class Net(nn.Cell):
+        def __init__(self):
+            super(Net, self).__init__()
+
+        def construct(self, x, y, z):
+            ret_x = x.dtype == mstype.float32
+            ret_y = y.dtype == mstype.int32
+            ret_z = z.dtype == mstype.bool_
+            ret_xy = x.dtype == y.dtype
+            ret_xz = x.dtype == z.dtype
+            ret_yz = y.dtype == z.dtype
+            return ret_x, ret_y, ret_z, ret_xy, ret_xz, ret_yz
+
+    net = Net()
+    x = Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)), mstype.float32)
+    y = Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)), mstype.int32)
+    z = Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)), mstype.bool_)
+    assert net(x, y, z) == (True, True, True, False, False, False)

tests/ut/python/pynative_mode/test_implicit_conversion.py

@@ -96,7 +96,7 @@ def test_float_tensor_and_str_add():
     y = "ok"
     with pytest.raises(TypeError) as er:
         ret = x + y
-    assert "For 'TensorAdd', the 1th input is a not support type: str" in str(er.value)
+    assert "For 'TensorAdd', the 1th input is a not support implicit conversion type: str" in str(er.value)
 
 
 def test_float_tensor_and_tuple_add():
@@ -104,7 +104,7 @@ def test_float_tensor_and_tuple_add():
     y = (1, 2, 3)
     with pytest.raises(TypeError) as er:
         ret = x + y
-    assert "For 'TensorAdd', the 1th input is a not support type: tuple" in str(er.value)
+    assert "For 'TensorAdd', the 1th input is a not support implicit conversion type: tuple" in str(er.value)
 
 
 def test_float_tensor_and_list_add():
@@ -112,7 +112,7 @@ def test_float_tensor_and_list_add():
     y = [1, 2, 3]
     with pytest.raises(TypeError) as er:
         ret = x + y
-    assert "For 'TensorAdd', the 1th input is a not support type: list" in str(er.value)
+    assert "For 'TensorAdd', the 1th input is a not support implicit conversion type: list" in str(er.value)
 
 
 def test_float_tensor_and_bool_tensors_add_grad():