support operator ** // % for scalar and tensor, and in not in for dict, ang str concat

mindspore/_extends/parse/resources.py

@@ -83,9 +83,9 @@ convert_object_map = {
     T.mul:          multitype_ops.mul,
     T.truediv:      multitype_ops.div,
     T.getitem:      multitype_ops.getitem,
-    T.floordiv:     NO_IMPLEMENT,
-    T.mod:          F.scalar_mod,
-    T.pow:          F.scalar_pow,
+    T.floordiv:     multitype_ops.floordiv,
+    T.mod:          multitype_ops.mod,
+    T.pow:          multitype_ops.pow_,
     T.matmul:       F.dot,
     T.lshift:       NO_IMPLEMENT,
     T.rshift:       NO_IMPLEMENT,
@@ -104,8 +104,8 @@ convert_object_map = {
     T.ge:           multitype_ops.greater_equal,
     T.is_:          F.is_,
     T.is_not:       F.is_not,
-    T.contains:     NO_IMPLEMENT,
-    T.not_contains: NO_IMPLEMENT,
+    T.contains:     F.in_dict,
+    T.not_contains: F.not_in_dict,
 
     # system function
     T.len:          M.ms_len,

mindspore/ccsrc/operator/cc_implementations.cc

@@ -103,7 +103,7 @@ T InnerScalarMul(T x, T y) {
 }
 
 template <typename T>
-T InnerScalarDiv(T x, T y) {
+float InnerScalarDiv(T x, T y) {
   if (y == 0) {
     MS_LOG(EXCEPTION) << "Divisor could not be zero";
   }
@@ -111,23 +111,41 @@ T InnerScalarDiv(T x, T y) {
     MS_LOG(EXCEPTION) << "Overflow of the div of two signed number x: " << std::to_string(x)
                       << ", y: " << std::to_string(y) << ".";
   }
-  return x / y;
+  return static_cast<float>(x) / static_cast<float>(y);
 }
 
-int32_t InnerScalarMod(int32_t x, int32_t y) {
+template <typename T>
+T InnerScalarFloordiv(T x, T y) {
+  auto ret = std::floor(InnerScalarDiv(x, y));
+  if (std::is_integral<T>::value) {
+    return static_cast<int>(ret);
+  }
+  return ret;
+}
+
+template <typename T>
+T InnerScalarMod(T x, T y) {
   if (y == 0) {
     MS_LOG(EXCEPTION) << "Could not mod to zero.";
   }
-  if (IsSignedIntOverflow(x, y, OpType::MOD)) {
+  if (std::is_integral<T>::value && std::is_signed<T>::value && IsSignedIntOverflow(x, y, OpType::MOD)) {
     MS_LOG(EXCEPTION) << "Overflow of the mod of two signed number x: " << std::to_string(x)
                       << ", y: " << std::to_string(y) << ".";
   }
-  return x % y;
+  if (std::is_integral<T>::value) {
+    return static_cast<int>(x) % static_cast<int>(y);
+  }
+  float x_int = std::floor(x);
+  float y_int = std::ceil(y);
+  float max = x_int / y_int;
+  float ret = x - y * max;
+  return ret;
 }
 
-float InnerScalarMod(float, float) { MS_LOG(EXCEPTION) << "Float does not support mod operator."; }
-
-double InnerScalarMod(double, double) { MS_LOG(EXCEPTION) << "Double does not support mod operator."; }
+template <typename T, typename U>
+T InnerScalarPow(T x, U y) {
+  return std::pow(x, y);
+}
 
 template <typename T, typename U>
 bool InnerScalarEq(T x, U y) {
@@ -193,6 +211,8 @@ SCALAR_OP(Sub)
 SCALAR_OP(Mul)
 SCALAR_OP(Div)
 SCALAR_OP(Mod)
+SCALAR_OP(Pow)
+SCALAR_OP(Floordiv)
 
 #define LOGIC_OP(op_t)                                                                       \
   ValuePtr Scalar##op_t(const ValuePtrList& list) {                                          \
@@ -227,6 +247,10 @@ SCALAR_OP(Mod)
       bool sum = InnerScalar##op_t(GetValue<float>(x), GetValue<int>(y));                    \
       return MakeValue(sum);                                                                 \
     }                                                                                        \
+    if (x->isa<Int32Imm>() && y->isa<FP32Imm>()) {                                           \
+      bool sum = InnerScalar##op_t(GetValue<int>(x), GetValue<float>(y));                    \
+      return MakeValue(sum);                                                                 \
+    }                                                                                        \
     if (x->isa<Int64Imm>() && y->isa<Int32Imm>()) {                                          \
       bool sum = InnerScalar##op_t(GetValue<int64_t>(x), GetValue<int>(y));                  \
       return MakeValue(sum);                                                                 \

mindspore/ccsrc/operator/cc_implementations.h

@@ -37,9 +37,10 @@ ValuePtr ScalarSub(const ValuePtrList& list);
 ValuePtr ScalarMul(const ValuePtrList& list);
 ValuePtr ScalarDiv(const ValuePtrList& list);
 ValuePtr ScalarMod(const ValuePtrList& list);
+ValuePtr ScalarPow(const ValuePtrList& list);
+ValuePtr ScalarFloordiv(const ValuePtrList& list);
 ValuePtr ScalarUAdd(const ValuePtrList& list);
 ValuePtr ScalarUSub(const ValuePtrList& list);
-ValuePtr ScalarUSub(const ValuePtrList& list);
 ValuePtr ScalarLog(const ValuePtrList& list);
 ValuePtr ScalarEq(const ValuePtrList& list);
 ValuePtr ScalarLt(const ValuePtrList& list);

mindspore/ccsrc/operator/composite/do_signature.cc

@@ -88,14 +88,17 @@ std::map<SignatureEnumDType, size_t> GetMaxDtypeIndex(const std::vector<Signatur
     if (indexs.size() < 2) {
       continue;
     }
-    size_t m_index = indexs[0];
-    for (size_t i = 1; i < indexs.size(); ++i) {
-      if (args_spec_list[indexs[i]]->isa<abstract::AbstractTensor>()) {
-        m_index = indexs[i];
+
+    for (const auto& index : indexs) {
+      AbstractBasePtr arg_value = args_spec_list[index];
+      if (arg_value->isa<abstract::AbstractRef>()) {
+        arg_value = arg_value->cast<abstract::AbstractRefPtr>()->ref();
+      }
+
+      if (arg_value->isa<abstract::AbstractTensor>()) {
+        (void)dst_type.insert(std::make_pair(type, index));
+        break;
       }
-    }
-    if (args_spec_list[m_index]->isa<abstract::AbstractTensor>()) {
-      (void)dst_type.insert(std::make_pair(type, m_index));
     }
   }
   return dst_type;
@@ -119,15 +122,19 @@ void DoAutoCast(const std::vector<Signature>& signature, const abstract::Abstrac
   (void)std::transform(signature.begin(), signature.end(), std::back_inserter(dtypes),
                        [](const Signature& sig) { return sig.dtype; });
   int empty_dtype_count = std::count(dtypes.begin(), dtypes.end(), SignatureEnumDType::kDTypeEmptyDefaultValue);
-  if (dtypes.size() == 0 || static_cast<int>(dtypes.size()) == empty_dtype_count) {
+  if (dtypes.empty() || static_cast<int>(dtypes.size()) == empty_dtype_count) {
     return;
   }
   // Stat the index of the arguments with the largest type in the same SignatureEnumDType.
   std::map<SignatureEnumDType, size_t> dst_type = GetMaxDtypeIndex(dtypes, args_spec_list);
   // Identify which arg requires auto cast
   for (size_t i = 0; i < args_spec_list.size(); ++i) {
+    AbstractBasePtr arg_value = args_spec_list[i];
+    if (arg_value->isa<abstract::AbstractRef>()) {
+      arg_value = arg_value->cast<abstract::AbstractRefPtr>()->ref();
+    }
     auto it = dst_type.find(dtypes[i]);
-    if (it == dst_type.end() || it->second == i || !args_spec_list[i]->isa<abstract::AbstractScalar>()) {
+    if (it == dst_type.end() || it->second == i || !arg_value->isa<abstract::AbstractScalar>()) {
       continue;
     }
     // get source node for cast

mindspore/ccsrc/operator/ops.cc

@@ -28,6 +28,7 @@ const PrimitivePtr kPrimScalarAdd = std::make_shared<Primitive>("scalar_add");
 const PrimitivePtr kPrimScalarSub = std::make_shared<Primitive>("scalar_sub");
 const PrimitivePtr kPrimScalarMul = std::make_shared<Primitive>("scalar_mul");
 const PrimitivePtr kPrimScalarDiv = std::make_shared<Primitive>("scalar_div");
+const PrimitivePtr kPrimScalarFloordiv = std::make_shared<Primitive>("scalar_floordiv");
 const PrimitivePtr kPrimScalarMod = std::make_shared<Primitive>("scalar_mod");
 const PrimitivePtr kPrimScalarPow = std::make_shared<Primitive>("scalar_pow");
 const PrimitivePtr kPrimScalarTrunc = std::make_shared<Primitive>("scalar_trunc");
@@ -78,6 +79,7 @@ const PrimitivePtr kPrimCreateInstance = std::make_shared<Primitive>("create_ins
 
 // Structure
 const PrimitivePtr kPrimStringEqual = std::make_shared<Primitive>("string_equal");
+const PrimitivePtr kPrimStringConcat = std::make_shared<Primitive>("string_concat");
 const PrimitivePtr kPrimMakeTuple = std::make_shared<Primitive>("make_tuple");
 const PrimitivePtr kPrimMakeList = std::make_shared<Primitive>("make_list");
 const PrimitivePtr kPrimMakeDict = std::make_shared<Primitive>("make_dict");
@@ -221,6 +223,8 @@ const PrimitivePtr kPrimBroadcastGradientArgs = std::make_shared<Primitive>("Bro
 const PrimitivePtr kPrimControlDepend = std::make_shared<Primitive>("ControlDepend");
 const PrimitivePtr kPrimIs_ = std::make_shared<Primitive>("is_");
 const PrimitivePtr kPrimIsNot = std::make_shared<Primitive>("is_not");
+const PrimitivePtr kPrimInDict = std::make_shared<Primitive>("in_dict");
+const PrimitivePtr kPrimNotInDict = std::make_shared<Primitive>("not_in_dict");
 
 // Comm ops
 const PrimitivePtr kPrimMirror = std::make_shared<Primitive>("_MirrorOperator");

mindspore/ccsrc/operator/ops.h

@@ -34,6 +34,7 @@ extern const PrimitivePtr kPrimScalarAdd;
 extern const PrimitivePtr kPrimScalarSub;
 extern const PrimitivePtr kPrimScalarMul;
 extern const PrimitivePtr kPrimScalarDiv;
+extern const PrimitivePtr kPrimScalarFloordiv;
 extern const PrimitivePtr kPrimScalarMod;
 extern const PrimitivePtr kPrimScalarPow;
 extern const PrimitivePtr kPrimScalarTrunc;
@@ -84,6 +85,7 @@ extern const PrimitivePtr kPrimCreateInstance;
 
 // Structure
 extern const PrimitivePtr kPrimStringEqual;
+extern const PrimitivePtr kPrimStringConcat;
 extern const PrimitivePtr kPrimMakeTuple;
 extern const PrimitivePtr kPrimMakeList;
 extern const PrimitivePtr kPrimMakeDict;
@@ -227,8 +229,8 @@ extern const PrimitivePtr kPrimBroadcastGradientArgs;
 extern const PrimitivePtr kPrimControlDepend;
 extern const PrimitivePtr kPrimIs_;
 extern const PrimitivePtr kPrimIsNot;
-extern const PrimitivePtr kPrimMinimumGrad;
-extern const PrimitivePtr kPrimMaximumGrad;
+extern const PrimitivePtr kPrimInDict;
+extern const PrimitivePtr kPrimNotInDict;
 
 // Comm ops
 extern const PrimitivePtr kPrimMirror;

mindspore/ccsrc/operator/prim_nn.cc

@@ -114,12 +114,12 @@ void FusedBatchNormCheckDim(const PrimitivePtr &primitive, const AbstractBasePtr
     AbstractTensorPtr arg = CheckArg<AbstractTensor>(op_name, args_spec_list, i);
     ShapePtr arg_shape = dyn_cast<Shape>(arg->GetShapeTrack());
     if (arg_shape == nullptr) {
-      MS_LOG(EXCEPTION) << "" << op_name << " type of args[" << i << "] should be Shape, but " << arg->ToString();
+      MS_LOG(EXCEPTION) << op_name << " type of args[" << i << "] should be Shape, but " << arg->ToString();
     }
 
     if (i == 0) {
       if (arg_shape->shape().size() < 2) {
-        MS_LOG(EXCEPTION) << "" << op_name << " shape of args[" << i
+        MS_LOG(EXCEPTION) << op_name << " shape of args[" << i
                           << "] should be TensorShape with dimension greater than 1, but shape: "
                           << arg_shape->ToString();
       }
@@ -127,7 +127,7 @@ void FusedBatchNormCheckDim(const PrimitivePtr &primitive, const AbstractBasePtr
     }
 
     if (arg_shape->shape().size() != 1) {
-      MS_LOG(EXCEPTION) << "" << op_name << " shape of args[" << i
+      MS_LOG(EXCEPTION) << op_name << " shape of args[" << i
                         << "] should be TensorShape with dimension: 1, but shape: " << arg_shape->ToString();
     }
   }
@@ -159,7 +159,7 @@ AbstractBasePtr InferImplFusedBatchNorm(const AnalysisEnginePtr &, const Primiti
       MS_LOG(EXCEPTION) << "Arg shape size should >= 1.";
     }
     if (arg_shape_list[0] != input_shape_list[1]) {
-      MS_LOG(EXCEPTION) << "" << op_name << " size of tensor param[" << i << "](which is " << arg_shape_list[0]
+      MS_LOG(EXCEPTION) << op_name << " size of tensor param[" << i << "](which is " << arg_shape_list[0]
                         << ") should match the second dimension of tensor"
                            " param[0](which is "
                         << input_shape_list[1] << ").";
@@ -378,7 +378,7 @@ AbstractBasePtr InferImplDropoutGenMask(const AnalysisEnginePtr &, const Primiti
 
   TypePtr prob_type = keep_prob->element()->BuildType();
   if ((prob_type->type_id() != kNumberTypeFloat16) && (prob_type->type_id() != kNumberTypeFloat32)) {
-    MS_LOG(EXCEPTION) << "" << op_name << " keep_prob type should be float16 or float32, but " << prob_type->ToString()
+    MS_LOG(EXCEPTION) << op_name << " keep_prob type should be float16 or float32, but " << prob_type->ToString()
                       << ".";
   }
 

mindspore/ccsrc/operator/prim_statement.cc

@@ -169,5 +169,36 @@ AbstractBasePtr InferImplIsNot(const AnalysisEnginePtr &, const PrimitivePtr &pr
 
   return std::make_shared<AbstractScalar>(!(*t == *x));
 }
+
+bool IsInDict(const PrimitivePtr &primitive, const AbstractBasePtrList &args_spec_list) {
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 2);
+  auto key = CheckArg<AbstractScalar>(op_name, args_spec_list, 0);
+  auto dict = CheckArg<AbstractDictionary>(op_name, args_spec_list, 1);
+
+  ValuePtr key_value = key->BuildValue();
+  if (!key_value->isa<StringImm>()) {
+    MS_LOG(EXCEPTION) << op_name << " evaluator key should be string, but got " << key_value->ToString();
+  }
+  auto key_str = GetValue<std::string>(key_value);
+  std::vector<AbstractAttribute> dict_elems = dict->elements();
+  auto it = std::find_if(dict_elems.begin(), dict_elems.end(),
+                         [key_str](const AbstractAttribute &item) { return item.first == key_str; });
+  return it != dict_elems.end();
+}
+
+AbstractBasePtr InferImplInDict(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                const AbstractBasePtrList &args_spec_list) {
+  // statement: x in t
+  // Inputs: x, t
+  return std::make_shared<AbstractScalar>(IsInDict(primitive, args_spec_list));
+}
+
+AbstractBasePtr InferImplNotInDict(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                   const AbstractBasePtrList &args_spec_list) {
+  // statement: x not in t
+  // Inputs: x, t
+  return std::make_shared<AbstractScalar>(!IsInDict(primitive, args_spec_list));
+}
 }  // namespace abstract
 }  // namespace mindspore

mindspore/ccsrc/operator/prim_structures.cc

@@ -36,7 +36,7 @@ AbstractBasePtr InferImplStringEqual(const AnalysisEnginePtr &, const PrimitiveP
   ValuePtr value_x = scalar_x->BuildValue();
   ValuePtr value_y = scalar_y->BuildValue();
   if (!value_x->isa<StringImm>() || !value_y->isa<StringImm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " requires 2 parameters are string, but got param0: " << value_x->ToString()
+    MS_LOG(EXCEPTION) << op_name << " requires 2 parameters are string, but got param0: " << value_x->ToString()
                       << ", param1: " << value_y->ToString();
   }
 
@@ -44,6 +44,25 @@ AbstractBasePtr InferImplStringEqual(const AnalysisEnginePtr &, const PrimitiveP
   return std::make_shared<AbstractScalar>(ret);
 }
 
+AbstractBasePtr InferImplStringConcat(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                      const AbstractBasePtrList &args_spec_list) {
+  // Inputs: two scalars whose value is a string.
+  const std::string op_name = primitive->name();
+  CheckArgsSize(op_name, args_spec_list, 2);
+  AbstractScalarPtr scalar_x = CheckArg<AbstractScalar>(op_name, args_spec_list, 0);
+  AbstractScalarPtr scalar_y = CheckArg<AbstractScalar>(op_name, args_spec_list, 1);
+
+  ValuePtr value_x = scalar_x->BuildValue();
+  ValuePtr value_y = scalar_y->BuildValue();
+  if (!value_x->isa<StringImm>() || !value_y->isa<StringImm>()) {
+    MS_LOG(EXCEPTION) << op_name << " requires 2 parameters are string, but got param0: " << value_x->ToString()
+                      << ", param1: " << value_y->ToString();
+  }
+
+  std::string ret = (value_x->cast<StringImmPtr>()->value() + value_y->cast<StringImmPtr>()->value());
+  return std::make_shared<AbstractScalar>(ret);
+}
+
 AbstractBasePtr InferImplMakeTuple(const AnalysisEnginePtr &, const PrimitivePtr &,
                                    const AbstractBasePtrList &args_spec_list) {
   return std::make_shared<AbstractTuple>(args_spec_list);
@@ -64,7 +83,7 @@ AbstractBasePtr InferImplMakeDict(const AnalysisEnginePtr &, const PrimitivePtr
 
   size_t keys_size = keys->size();
   if (values->size() != keys_size) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator keys' size is not equal with values' size";
+    MS_LOG(EXCEPTION) << op_name << " evaluator keys' size is not equal with values' size";
   }
 
   std::vector<AbstractAttribute> key_value;
@@ -76,7 +95,7 @@ AbstractBasePtr InferImplMakeDict(const AnalysisEnginePtr &, const PrimitivePtr
     ValuePtr keyPtr = key->BuildValue();
     MS_EXCEPTION_IF_NULL(keyPtr);
     if (!keyPtr->isa<StringImm>()) {
-      MS_LOG(EXCEPTION) << "" << op_name << " evaluator keys should be string, but got " << keyPtr->ToString();
+      MS_LOG(EXCEPTION) << op_name << " evaluator keys should be string, but got " << keyPtr->ToString();
     }
     std::string key_string = GetValue<std::string>(keyPtr);
     key_value.emplace_back(key_string, value_list[index]);
@@ -93,7 +112,7 @@ AbstractBasePtr InferImplMakeKwarg(const AnalysisEnginePtr &, const PrimitivePtr
 
   ValuePtr keyPtr = key->BuildValue();
   if (!keyPtr->isa<StringImm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator key should be string, but got " << keyPtr->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator key should be string, but got " << keyPtr->ToString();
   }
   std::string key_string = GetValue<std::string>(keyPtr);
   return std::make_shared<AbstractKeywordArg>(key_string, args_spec_list[1]);
@@ -109,14 +128,13 @@ AbstractBasePtr InferImplExtractKwarg(const AnalysisEnginePtr &, const Primitive
 
   ValuePtr key_value = key->BuildValue();
   if (!key_value->isa<StringImm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator key should be string, but got " << key_value->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator key should be string, but got " << key_value->ToString();
   }
   std::string key_input = GetValue<std::string>(key_value);
   std::string key_actual = kwarg->get_key();
   if (key_actual != key_input) {
-    MS_LOG(EXCEPTION) << "" << op_name
-                      << " evaluator input key should be same as AbstractKeywordArg' key, but input is " << key_input
-                      << ", AbstractKeywordArg' key is " << key_actual;
+    MS_LOG(EXCEPTION) << op_name << " evaluator input key should be same as AbstractKeywordArg' key, but input is "
+                      << key_input << ", AbstractKeywordArg' key is " << key_actual;
   }
   return kwarg->get_arg();
 }
@@ -187,13 +205,12 @@ AbstractBasePtr InferTupleOrListGetItem(const std::string &op_name, const Abstra
 
   ValuePtr index_value = index->BuildValue();
   if (!index_value->isa<Int32Imm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator index should be an int32 number, but got "
-                      << index_value->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator index should be an int32 number, but got " << index_value->ToString();
   }
   int idx_v = GetValue<int>(index_value);
   std::size_t nelems = queue->elements().size();
   if (idx_v >= SizeToInt(nelems) || idx_v < -SizeToInt(nelems)) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator index should be in range[-" << SizeToInt(nelems) << ", "
+    MS_LOG(EXCEPTION) << op_name << " evaluator index should be in range[-" << SizeToInt(nelems) << ", "
                       << SizeToInt(nelems) << "), but got " << idx_v << ".";
   }
 
@@ -215,8 +232,7 @@ AbstractBasePtr InferTupleOrListSetItem(const std::string &op_name, const Abstra
 
   ValuePtr index_value = index->BuildValue();
   if (!index_value->isa<Int32Imm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator index should be an int32 number, but got "
-                      << index_value->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator index should be an int32 number, but got " << index_value->ToString();
   }
   int idx_v = GetValue<int>(index_value);
   if (idx_v < 0) {
@@ -227,8 +243,7 @@ AbstractBasePtr InferTupleOrListSetItem(const std::string &op_name, const Abstra
   AbstractBasePtrList elements = queue->elements();
   std::size_t nelems = elements.size();
   if (uidx_v >= nelems) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator the index: " << uidx_v << " to set out of range: " << nelems - 1
-                      << ".";
+    MS_LOG(EXCEPTION) << op_name << " evaluator the index: " << uidx_v << " to set out of range: " << nelems - 1 << ".";
   }
   elements[uidx_v] = args_spec_list[2];
   return std::make_shared<T>(elements);
@@ -264,12 +279,12 @@ AbstractBasePtr InferImplDictGetItem(const AnalysisEnginePtr &, const PrimitiveP
 
   ValuePtr key_value = key->BuildValue();
   if (!key_value->isa<StringImm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator key should be string, but got " << key_value->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator key should be string, but got " << key_value->ToString();
   }
-  std::string key_str = GetValue<std::string>(key_value);
+  auto key_str = GetValue<std::string>(key_value);
   std::vector<AbstractAttribute> dict_elems = dict->elements();
   auto it = std::find_if(dict_elems.begin(), dict_elems.end(),
-                         [key_str](AbstractAttribute &item) { return item.first == key_str; });
+                         [key_str](const AbstractAttribute &item) { return item.first == key_str; });
 
   if (it == dict_elems.end()) {
     MS_LOG(EXCEPTION) << "The key " << key_str << " does not exist in the dict:" << args_spec_list[0]->ToString();
@@ -287,7 +302,7 @@ AbstractBasePtr InferImplDictSetItem(const AnalysisEnginePtr &, const PrimitiveP
 
   ValuePtr key_value = key->BuildValue();
   if (!key_value->isa<StringImm>()) {
-    MS_LOG(EXCEPTION) << "" << op_name << " evaluator key should be string, but got " << key_value->ToString();
+    MS_LOG(EXCEPTION) << op_name << " evaluator key should be string, but got " << key_value->ToString();
   }
   std::string key_str = GetValue<std::string>(key_value);
   std::vector<AbstractAttribute> dict_elems = dict->elements();
@@ -446,27 +461,27 @@ AbstractBasePtr InferImplReduceShape(const AnalysisEnginePtr &, const PrimitiveP
 
   auto x_shp_value = shape_x->BuildValue();
   if (x_shp_value->isa<AnyValue>()) {
-    MS_LOG(EXCEPTION) << "" << op_name
+    MS_LOG(EXCEPTION) << op_name
                       << " evaluator shape's data field can't be anything: " << args_spec_list[1]->ToString();
   }
 
   // Axis can be scalar, tuple or None
   AbstractTuplePtr axis = nullptr;
   if (args_spec_list[1]->isa<AbstractScalar>()) {
-    MS_LOG(DEBUG) << "" << op_name << " evaluator second parameter is scalar";
+    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";
+    MS_LOG(DEBUG) << op_name << " evaluator second parameter is tuple";
     axis = args_spec_list[1]->cast<AbstractTuplePtr>();
   } 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, but got "
                       << args_spec_list[1]->ToString();
   }
 
   auto axis_value = axis->BuildValue();
   if (axis_value->isa<AnyValue>()) {
-    MS_LOG(EXCEPTION) << "" << op_name
+    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>();

mindspore/ccsrc/operator/prim_to_function.cc

@@ -24,36 +24,35 @@ namespace mindspore {
 namespace prim {
 
 PrimToFunction::PrimToFunction()
-    : prim_func_type_map_({
-        // ONE_ARG prim
-        {"bool_not", kPrimTypeOneArg},
-        {"scalar_cos", kPrimTypeOneArg},
-        {"scalar_exp", kPrimTypeOneArg},
-        {"scalar_floor", kPrimTypeOneArg},
-        {"scalar_log", kPrimTypeOneArg},
-        {"scalar_sin", kPrimTypeOneArg},
-        {"scalar_tan", kPrimTypeOneArg},
-        {"scalar_trunc", kPrimTypeOneArg},
-        {"typeof", kPrimTypeOneArg},
-        {"scalar_uadd", kPrimTypeOneArg},
-        {"scalar_usub", kPrimTypeOneArg},
-        // TWO_ARGS prim
-        {"scalar_add", kPrimTypeTwoArgs},
-        {"bool_and", kPrimTypeTwoArgs},
-        {"bool_eq", kPrimTypeTwoArgs},
-        {"bool_or", kPrimTypeTwoArgs},
-        {"scalar_div", kPrimTypeTwoArgs},
-        {"scalar_eq", kPrimTypeTwoArgs},
-        {"scalar_ge", kPrimTypeTwoArgs},
-        {"scalar_gt", kPrimTypeTwoArgs},
-        {"scalar_le", kPrimTypeTwoArgs},
-        {"scalar_lt", kPrimTypeTwoArgs},
-        {"scalar_ne", kPrimTypeTwoArgs},
-        {"scalar_mod", kPrimTypeTwoArgs},
-        {"scalar_mul", kPrimTypeTwoArgs},
-        {"scalar_pow", kPrimTypeTwoArgs},
-        {"scalar_sub", kPrimTypeTwoArgs},
-      }) {}
+    : prim_func_type_map_({// ONE_ARG prim
+                           {"bool_not", kPrimTypeOneArg},
+                           {"scalar_cos", kPrimTypeOneArg},
+                           {"scalar_exp", kPrimTypeOneArg},
+                           {"scalar_floor", kPrimTypeOneArg},
+                           {"scalar_log", kPrimTypeOneArg},
+                           {"scalar_sin", kPrimTypeOneArg},
+                           {"scalar_tan", kPrimTypeOneArg},
+                           {"scalar_trunc", kPrimTypeOneArg},
+                           {"typeof", kPrimTypeOneArg},
+                           {"scalar_uadd", kPrimTypeOneArg},
+                           {"scalar_usub", kPrimTypeOneArg},
+                           // TWO_ARGS prim
+                           {"scalar_add", kPrimTypeTwoArgs},
+                           {"bool_and", kPrimTypeTwoArgs},
+                           {"bool_eq", kPrimTypeTwoArgs},
+                           {"bool_or", kPrimTypeTwoArgs},
+                           {"scalar_div", kPrimTypeTwoArgs},
+                           {"scalar_eq", kPrimTypeTwoArgs},
+                           {"scalar_ge", kPrimTypeTwoArgs},
+                           {"scalar_gt", kPrimTypeTwoArgs},
+                           {"scalar_le", kPrimTypeTwoArgs},
+                           {"scalar_lt", kPrimTypeTwoArgs},
+                           {"scalar_ne", kPrimTypeTwoArgs},
+                           {"scalar_mod", kPrimTypeTwoArgs},
+                           {"scalar_mul", kPrimTypeTwoArgs},
+                           {"scalar_pow", kPrimTypeTwoArgs},
+                           {"scalar_sub", kPrimTypeTwoArgs},
+                           {"scalar_floordiv", kPrimTypeTwoArgs}}) {}
 
 bool PrimToFunction::GetFunction(const PrimitivePtr& prim, FunctionPtr* const func) const {
   bool result = false;

mindspore/ccsrc/pipeline/static_analysis/prim.cc

@@ -52,6 +52,8 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
     {prim::kPrimSwitch, {InferImplSwitch, true}},
     {prim::kPrimIs_, {InferImplIs_, true}},
     {prim::kPrimIsNot, {InferImplIsNot, true}},
+    {prim::kPrimInDict, {InferImplInDict, true}},
+    {prim::kPrimNotInDict, {InferImplNotInDict, true}},
     // Maths
     {prim::kPrimMaximumGrad, {InferImplMinOrMaxGrad, true}},
     {prim::kPrimMinimumGrad, {InferImplMinOrMaxGrad, true}},
@@ -91,6 +93,7 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
     {prim::kPrimMakeRange, {InferImplMakeRange, false}},
     {prim::kPrimStopGradient, {InferImplStopGradient, false}},
     {prim::kPrimStringEqual, {InferImplStringEqual, false}},
+    {prim::kPrimStringConcat, {InferImplStringConcat, false}},
     {prim::kPrimDictLen, {InferImplDictLen, false}},
     // NN
     {prim::kPrimPooling, {InferImplPooling, true}},
@@ -988,6 +991,8 @@ PrimitiveToImplMap &GetUniformPrimitiveToImplMap() {
     {prim::kPrimScalarMul, {prim::ScalarMul, true, nullptr, true}},
     {prim::kPrimScalarDiv, {prim::ScalarDiv, true, nullptr, true}},
     {prim::kPrimScalarMod, {prim::ScalarMod, true, nullptr, true}},
+    {prim::kPrimScalarPow, {prim::ScalarPow, true, nullptr, true}},
+    {prim::kPrimScalarFloordiv, {prim::ScalarFloordiv, true, nullptr, true}},
     {prim::kPrimScalarUadd, {prim::ScalarUAdd, true, nullptr, true}},
     {prim::kPrimScalarUsub, {prim::ScalarUSub, true, nullptr, true}},
     {prim::kPrimScalarLog, {prim::ScalarLog, true, nullptr, true}},

mindspore/ccsrc/pipeline/static_analysis/prim.h

@@ -178,6 +178,10 @@ AbstractBasePtr InferImplIs_(const AnalysisEnginePtr &, const PrimitivePtr &,
                              const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplIsNot(const AnalysisEnginePtr &, const PrimitivePtr &,
                                const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplInDict(const AnalysisEnginePtr &, const PrimitivePtr &,
+                                const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplNotInDict(const AnalysisEnginePtr &, const PrimitivePtr &,
+                                   const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplPooling(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                  const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplPoolingGrad(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
@@ -287,6 +291,8 @@ AbstractBasePtr InferImplStopGradient(const AnalysisEnginePtr &, const Primitive
                                       const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplStringEqual(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                      const AbstractBasePtrList &args_spec_list);
+AbstractBasePtr InferImplStringConcat(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                      const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplDictLen(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                  const AbstractBasePtrList &args_spec_list);
 

mindspore/ops/composite/multitype_ops/__init__.py

@@ -19,6 +19,9 @@ from .add_impl import add
 from .sub_impl import sub
 from .mul_impl import mul
 from .div_impl import div
+from .pow_impl import pow_
+from .floordiv_impl import floordiv
+from .mod_impl import mod
 from .getitem_impl import getitem
 from .zeros_like_impl import zeros_like
 from .ones_like_impl import ones_like
@@ -38,6 +41,9 @@ __all__ = [
     'sub',
     'mul',
     'div',
+    'pow_',
+    'floordiv',
+    'mod',
     'uadd',
     'zeros_like',
     'ones_like',

mindspore/ops/composite/multitype_ops/add_impl.py

@@ -69,6 +69,21 @@ def _scalar_add_scalar(x, y):
     return F.scalar_add(x, y)
 
 
+@add.register("String", "String")
+def _string_concat_string(x, y):
+    """
+    Concatenate the string y to the string x.
+
+    Args:
+        x (str): The first input string.
+        y (str): the second input string.
+
+    Returns:
+        str, concatenate the y to the x.
+    """
+    return F.string_concat(x, y)
+
+
 @add.register("Number", "Tensor")
 def _scalar_add_tensor(x, y):
     """
@@ -81,8 +96,7 @@ def _scalar_add_tensor(x, y):
     Returns:
         Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(x, F.dtype(y))
-    return F.tensor_add(z, y)
+    return F.tensor_add(x, y)
 
 
 @add.register("Tensor", "Number")
@@ -97,8 +111,7 @@ def _tensor_add_scalar(x, y):
     Returns:
         Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(y, F.dtype(x))
-    return F.tensor_add(x, z)
+    return F.tensor_add(x, y)
 
 
 @add.register("Tensor", "Tensor")

mindspore/ops/composite/multitype_ops/div_impl.py

@@ -68,8 +68,7 @@ def _scalar_div_tensor(x, y):
     Returns:
         Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(x, F.dtype(y))
-    return F.tensor_div(z, y)
+    return F.tensor_div(x, y)
 
 
 @div.register("Tensor", "Number")
@@ -84,5 +83,4 @@ def _tensor_div_scalar(x, y):
     Returns:
         Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(y, F.dtype(x))
-    return F.tensor_div(x, z)
+    return F.tensor_div(x, y)

mindspore/ops/composite/multitype_ops/floordiv_impl.py

+# 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.
+# ============================================================================
+
+"""Implementation for internal polymorphism `floordiv` operations."""
+
+from ...composite import base
+from ... import functional as F
+
+
+floordiv = base.MultitypeFuncGraph("floordiv")
+"""
+`floordiv` is a metafuncgraph object which will compute the floordiv of two objects
+using ".register" decorator.
+"""
+
+
+@floordiv.register("Number", "Number")
+def _floordiv_scalar(x, y):
+    """Returns x // y where x and y are all scalars."""
+    return F.scalar_floordiv(x, y)
+
+
+@floordiv.register("Tensor", "Tensor")
+def _floordiv_tensor(x, y):
+    """Returns x // y where x and y are all tensors and have save dtype."""
+    return F.tensor_floordiv(x, y)
+
+
+@floordiv.register("Tensor", "Number")
+def _tensor_floordiv_scalar(x, y):
+    """Returns x // y where x is a tensor and y is a scalar. x and y should have same dtype."""
+    return F.tensor_floordiv(x, y)
+
+
+@floordiv.register("Number", "Tensor")
+def _scalar_floordiv_tensor(x, y):
+    """Returns x // y where x is a scalar and y is a tensor. x and y should have same dtype."""
+    return F.tensor_floordiv(x, y)

mindspore/ops/composite/multitype_ops/mod_impl.py

+# 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.
+# ============================================================================
+
+"""Implementation for internal polymorphism `mod` operations."""
+
+from ...composite import base
+from ... import functional as F
+
+
+mod = base.MultitypeFuncGraph("mod")
+"""
+`mod` is a metafuncgraph object which will compute the mod of two objects
+using ".register" decorator.
+"""
+
+
+@mod.register("Number", "Number")
+def _mod_scalar(x, y):
+    """Returns x % y where x and y are all scalars."""
+    return F.scalar_mod(x, y)
+
+
+@mod.register("Tensor", "Tensor")
+def _mod_tensor(x, y):
+    """Returns x % y where x and y are all tensors and have save dtype."""
+    return F.tensor_mod(x, y)
+
+
+@mod.register("Tensor", "Number")
+def _tensor_mod_scalar(x, y):
+    """Returns x % y where x is a tensor and y is a scalar. x and y should have same dtype."""
+    return F.tensor_mod(x, y)
+
+
+@mod.register("Number", "Tensor")
+def _scalar_mod_tensor(x, y):
+    """Returns x % y where x is a scalar and y is a tensor. x and y should have same dtype."""
+    return F.tensor_mod(x, y)

mindspore/ops/composite/multitype_ops/mul_impl.py

@@ -56,8 +56,7 @@ def _scalar_mul_tensor(x, y):
     Outputs:
        Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(x, F.dtype(y))
-    return F.tensor_mul(z, y)
+    return F.tensor_mul(x, y)
 
 
 @mul.register("Tensor", "Number")
@@ -68,5 +67,4 @@ def _tensor_mul_scalar(x, y):
     Outputs:
         Tensor, has the same dtype as x.
     """
-    z = F.scalar_to_tensor(y, F.dtype(x))
-    return F.tensor_mul(x, z)
+    return F.tensor_mul(x, y)

mindspore/ops/composite/multitype_ops/pow_impl.py

+# 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.
+# ============================================================================
+
+"""Implementation for internal polymorphism `pow` operations."""
+
+from ...composite import base
+from ... import functional as F
+
+
+pow_ = base.MultitypeFuncGraph("pow")
+"""
+`pow` is a metafuncgraph object which will compute the pow of two objects
+using ".register" decorator.
+"""
+
+
+@pow_.register("Number", "Number")
+def _pow_scalar(x, y):
+    """Returns x ** y where x and y are all scalars."""
+    return F.scalar_pow(x, y)
+
+
+@pow_.register("Tensor", "Tensor")
+def _pow_tensor(x, y):
+    """Returns x ** y where x and y are all tensors and have save dtype."""
+    return F.tensor_pow(x, y)
+
+
+@pow_.register("Tensor", "Number")
+def _tensor_pow_scalar(x, y):
+    """Returns x ** y where x is a tensor and y is a scalar. x and y should have same dtype."""
+    return F.tensor_pow(x, y)
+
+
+@pow_.register("Number", "Tensor")
+def _scalar_pow_tensor(x, y):
+    """Returns x ** y where x is a scalar and y is a tensor. x and y should have same dtype."""
+    return F.tensor_pow(x, y)

mindspore/ops/composite/multitype_ops/sub_impl.py

@@ -41,12 +41,10 @@ def _sub_tensor(x, y):
 @sub.register("Number", "Tensor")
 def _scalar_sub_tensor(x, y):
     """Returns x - y where x is a scalar and y is a tensor. x and y should have same dtype."""
-    z = F.scalar_to_tensor(x, F.dtype(y))
-    return F.tensor_sub(z, y)
+    return F.tensor_sub(x, y)
 
 
 @sub.register("Tensor", "Number")
 def _tensor_sub_scalar(x, y):
     """Returns x - y where x is a tensor and y is a scalar. x and y should have same dtype."""
-    z = F.scalar_to_tensor(y, F.dtype(x))
-    return F.tensor_sub(x, z)
+    return F.tensor_sub(x, y)

mindspore/ops/functional.py

@@ -48,6 +48,9 @@ tensor_ge = P.GreaterEqual()
 tensor_sub = P.Sub()
 tensor_mul = P.Mul()
 tensor_div = P.RealDiv()
+tensor_floordiv = P.FloorDiv()
+tensor_pow = P.Pow()
+tensor_mod = P.FloorMod()
 strided_slice = P.StridedSlice()
 same_type_shape = P.SameTypeShape()
 equal = P.Equal()
@@ -83,6 +86,7 @@ scalar_add = Primitive('scalar_add')
 scalar_mul = Primitive('scalar_mul')
 scalar_sub = Primitive('scalar_sub')
 scalar_div = Primitive('scalar_div')
+scalar_floordiv = Primitive('scalar_floordiv')
 scalar_log = Primitive('scalar_log')
 scalar_pow = Primitive('scalar_pow')
 scalar_gt = Primitive('scalar_gt')
@@ -95,6 +99,7 @@ scalar_uadd = Primitive('scalar_uadd')
 scalar_usub = Primitive('scalar_usub')
 scalar_mod = Primitive('scalar_mod')
 string_eq = Primitive('string_equal')
+string_concat = Primitive('string_concat')
 bool_not = Primitive("bool_not")
 bool_or = Primitive("bool_or")
 bool_and = Primitive("bool_and")
@@ -104,7 +109,8 @@ logical_not = P.LogicalNot()
 array_to_scalar = Primitive('array_to_scalar')
 is_ = Primitive("is_")
 is_not = Primitive("is_not")
-
+in_dict = Primitive("in_dict")
+not_in_dict = Primitive("not_in_dict")
 broadcast_gradient_args = Primitive('BroadcastGradientArgs')
 dot = Primitive('dot')
 array_reduce = Primitive('array_reduce')

mindspore/ops/operations/math_ops.py

@@ -667,8 +667,8 @@ class AddN(PrimitiveWithInfer):
         >>>         return self.addN(z)
         >>>
         >>> net = NetAddN()
-        >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
-        >>> input_y = Tensor(np.array([4, 5, 6]), mindspore.int32)
+        >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float32)
+        >>> input_y = Tensor(np.array([4, 5, 6]), mindspore.float32)
         >>> net(input_x, input_y, input_x, input_y)
         Tensor([10, 14, 18], shape=(3,), dtype=mindspore.int32)
     """

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

@@ -131,3 +131,72 @@ def test_ME_arithmetic_operator_0070():
 def test_ME_logical_operator_0020():
     """ test_ME_logical_operator_0020 """
     logical_operator_base('or')
+
+
+def test_ops():
+    class OpsNet(Cell):
+        """ OpsNet definition """
+
+        def __init__(self, x, y):
+            super(OpsNet, self).__init__()
+            self.x = x
+            self.y = y
+            self.int = 4
+            self.float = 3.2
+            self.str_a = "hello"
+            self.str_b = "world"
+
+        def construct(self, x, y):
+            h = x // y
+            m = x ** y
+            n = x % y
+            r = self.x // self.y
+            s = self.x ** self.y
+            t = self.x % self.y
+            p = h + m + n
+            q = r + s + t
+            ret_pow = p ** q + q ** p
+            ret_mod = p % q + q % p
+            ret_floor = p // q + q // p
+            ret = ret_pow + ret_mod + ret_floor
+            if self.int > self.float:
+                if self.str_a + self.str_b == "helloworld":
+                    return ret
+            return x
+
+    net = OpsNet(9, 2)
+    x = Tensor(np.random.randint(low=1, high=10, size=(2, 3, 4), dtype=np.int32))
+    y = Tensor(np.random.randint(low=10, high=20, size=(2, 3, 4), dtype=np.int32))
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
+    net(x, y)
+
+
+def test_in_dict():
+    class InDictNet(Cell):
+        """ InDictNet definition """
+
+        def __init__(self, key_in, key_not_in):
+            super(InDictNet, self).__init__()
+            self.key_in = key_in
+            self.key_not_in = key_not_in
+
+        def construct(self, x, y, z):
+            d = {"a": x, "b": y}
+            ret_in = 1
+            ret_not_in = 2
+            if self.key_in in d:
+                ret_in = d[self.key_in]
+            if self.key_not_in not in d:
+                ret_not_in = z
+            ret = ret_in + ret_not_in
+            return ret
+
+    net = InDictNet("a", "c")
+    x = Tensor(np.random.randint(low=1, high=10, size=(2, 3, 4), dtype=np.int32))
+    y = Tensor(np.random.randint(low=10, high=20, size=(2, 3, 4), dtype=np.int32))
+    z = Tensor(np.random.randint(low=20, high=30, size=(2, 3, 4), dtype=np.int32))
+    context.set_context(mode=context.GRAPH_MODE)
+    net(x, y, z)
+
+
+