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1、Optimize the error message in operator 2、add operator test cases

This commit is contained in:
Margaret_wangrui 2021-10-11 11:36:22 +08:00
parent a9fbb25530
commit 7b93e4e87c
14 changed files with 832 additions and 131 deletions
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175
mindspore/ccsrc/frontend/operator/cc_implementations.cc
View File

@ -125,7 +125,7 @@ T InnerScalarMul(T x, T y) {
template <typename T>
float InnerScalarDiv(T x, T y) {
if (y == 0) {
MS_LOG(EXCEPTION) << "Divisor could not be zero";
MS_LOG(EXCEPTION) << "The divisor could not be zero.";
}
if (std::is_integral<T>::value && std::is_signed<T>::value && IsSignedIntOverflow(x, y, OpType::DIV)) {
MS_LOG(EXCEPTION) << "Overflow of the div of two signed number x: " << std::to_string(x)
@ -199,7 +199,7 @@ bool InnerScalarGe(T x, U y) {
ValuePtr Scalar##op_t(const ValuePtrList &list) { \
do { \
if (list.size() < 2) { \
MS_LOG(EXCEPTION) << "length of input list for Scalar" << #op_t << " is less than 2."; \
MS_LOG(EXCEPTION) << "The length of input list for Scalar" << #op_t << " is less than 2."; \
} \
ValuePtr x = list[0]; \
ValuePtr y = list[1]; \
@ -267,73 +267,75 @@ SCALAR_OP(Mod)
SCALAR_OP(Pow)
SCALAR_OP(Floordiv)
#define LOGIC_OP(op_t) \
ValuePtr Scalar##op_t(const ValuePtrList &list) { \
if (list.size() < 2) { \
MS_LOG(EXCEPTION) << "length of input list for Scalar" << #op_t << " is less than 2."; \
} \
ValuePtr x = list[0]; \
ValuePtr y = list[1]; \
MS_EXCEPTION_IF_NULL(x); \
MS_EXCEPTION_IF_NULL(y); \
if (x->isa<FP64Imm>() && y->isa<FP64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<double>(x), GetValue<double>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP32Imm>() && y->isa<FP32Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<float>(x), GetValue<float>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP64Imm>() && y->isa<FP32Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<double>(x), GetValue<float>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP32Imm>() && y->isa<FP64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<float>(x), GetValue<double>(y)); \
return MakeValue(sum); \
} \
if (x->isa<Int32Imm>() && y->isa<Int32Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<int>(x), GetValue<int>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP32Imm>() && y->isa<Int32Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<float>(x), GetValue<int>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP32Imm>() && y->isa<Int64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<float>(x), GetValue<int64_t>(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<FP32Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<int64_t>(x), GetValue<float>(y)); \
return MakeValue(sum); \
} \
if (x->isa<Int64Imm>() && y->isa<Int64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<int64_t>(x), GetValue<int64_t>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP64Imm>() && y->isa<Int64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<double>(x), GetValue<int64_t>(y)); \
return MakeValue(sum); \
} \
if (x->isa<Int64Imm>() && y->isa<FP64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<int64_t>(x), GetValue<double>(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); \
} \
if (x->isa<Int32Imm>() && y->isa<Int64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<int>(x), GetValue<int64_t>(y)); \
return MakeValue(sum); \
} \
MS_LOG(EXCEPTION) << "Unsupported Value for Scalar" << #op_t << ", x: " << x->ToString() \
<< ", y: " << y->ToString() << "."; \
#define LOGIC_OP(op_t) \
ValuePtr Scalar##op_t(const ValuePtrList &list) { \
constexpr size_t kListInputSize = 2; \
if (list.size() < kListInputSize) { \
MS_LOG(EXCEPTION) << "The length of input list for Scalar" << #op_t << " is less than 2."; \
} \
ValuePtr x = list[0]; \
ValuePtr y = list[1]; \
MS_EXCEPTION_IF_NULL(x); \
MS_EXCEPTION_IF_NULL(y); \
if (x->isa<FP64Imm>() && y->isa<FP64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<double>(x), GetValue<double>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP32Imm>() && y->isa<FP32Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<float>(x), GetValue<float>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP64Imm>() && y->isa<FP32Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<double>(x), GetValue<float>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP32Imm>() && y->isa<FP64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<float>(x), GetValue<double>(y)); \
return MakeValue(sum); \
} \
if (x->isa<Int32Imm>() && y->isa<Int32Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<int>(x), GetValue<int>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP32Imm>() && y->isa<Int32Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<float>(x), GetValue<int>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP32Imm>() && y->isa<Int64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<float>(x), GetValue<int64_t>(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<FP32Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<int64_t>(x), GetValue<float>(y)); \
return MakeValue(sum); \
} \
if (x->isa<Int64Imm>() && y->isa<Int64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<int64_t>(x), GetValue<int64_t>(y)); \
return MakeValue(sum); \
} \
if (x->isa<FP64Imm>() && y->isa<Int64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<double>(x), GetValue<int64_t>(y)); \
return MakeValue(sum); \
} \
if (x->isa<Int64Imm>() && y->isa<FP64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<int64_t>(x), GetValue<double>(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); \
} \
if (x->isa<Int32Imm>() && y->isa<Int64Imm>()) { \
bool sum = InnerScalar##op_t(GetValue<int>(x), GetValue<int64_t>(y)); \
return MakeValue(sum); \
} \
MS_LOG(EXCEPTION) << "Unsupported input type for Scalar" << #op_t << ", type of x:" << x->type_name() \
<< ", value of x:" << x->ToString() << ", type of y:" << y->type_name() \
<< ", value of y:" << y->ToString(); \
}
LOGIC_OP(Eq)
@ -372,12 +374,12 @@ ValuePtr ScalarUSub(const ValuePtrList &list) {
return MakeValue(sum);
}
MS_LOG(EXCEPTION) << "Unsported Value for ScalarUSub, x: " << x->ToString() << ".";
MS_LOG(EXCEPTION) << "Unsupported Value for ScalarUSub, x: " << x->ToString() << ".";
}
ValuePtr ScalarLog(const ValuePtrList &list) {
if (list.empty()) {
MS_LOG(EXCEPTION) << "Input list of ScalarLog is empty.";
if (list.size() != 1) {
MS_LOG(EXCEPTION) << "Input number of ScalarLog should be 1, but got " << list.size();
}
ValuePtr x = list[0];
MS_EXCEPTION_IF_NULL(x);
@ -391,12 +393,12 @@ ValuePtr ScalarLog(const ValuePtrList &list) {
return MakeValue(v);
}
MS_LOG(EXCEPTION) << "Unsported Value for ScalarLog, x: " << x->ToString();
MS_LOG(EXCEPTION) << "Unsupported Value for ScalarLog, x: " << x->ToString();
}
ValuePtr BoolNot(const ValuePtrList &list) {
if (list.empty()) {
MS_LOG(EXCEPTION) << "value list of BoolNot is empty";
if (list.size() != 1) {
MS_LOG(EXCEPTION) << "Input number of BoolNot should be 1, but got " << list.size();
}
ValuePtr x = list[0];
MS_EXCEPTION_IF_NULL(x);
@ -407,12 +409,13 @@ ValuePtr BoolNot(const ValuePtrList &list) {
return MakeValue(res);
}
MS_LOG(EXCEPTION) << "Unsported Value for BoolNot, x: " << x->ToString();
MS_LOG(EXCEPTION) << "Unsupported Value for BoolNot, x: " << x->ToString();
}
ValuePtr BoolAnd(const ValuePtrList &list) {
if (list.size() < 2) {
MS_LOG(EXCEPTION) << "Input number " << list.size() << " of BoolAnd is less then 2.";
constexpr size_t kListInputSize = 2;
if (list.size() != kListInputSize) {
MS_LOG(EXCEPTION) << "Input number of BoolAnd must be 2, but got " << list.size();
}
ValuePtr x = list[0];
ValuePtr y = list[1];
@ -426,12 +429,13 @@ ValuePtr BoolAnd(const ValuePtrList &list) {
return MakeValue(res);
}
MS_LOG(EXCEPTION) << "Unsported Value for BoolAnd, x: " << x->ToString() << ".";
MS_LOG(EXCEPTION) << "Unsupported Value for BoolAnd, x: " << x->ToString() << " y: " << y->ToString() << ".";
}
ValuePtr BoolOr(const ValuePtrList &list) {
if (list.size() < 2) {
MS_LOG(EXCEPTION) << "Input number " << list.size() << " of BoolOr is less then 2.";
constexpr size_t kListInputSize = 2;
if (list.size() != kListInputSize) {
MS_LOG(EXCEPTION) << "Input number of BoolOr must be 2, but got " << list.size();
}
ValuePtr x = list[0];
ValuePtr y = list[1];
@ -445,12 +449,13 @@ ValuePtr BoolOr(const ValuePtrList &list) {
return MakeValue(res);
}
MS_LOG(EXCEPTION) << "Unsported Value for BoolOr, x: " << x->ToString() << ".";
MS_LOG(EXCEPTION) << "Unsupported Value for BoolOr, x: " << x->ToString() << " y: " << y->ToString() << ".";
}
ValuePtr BoolEq(const ValuePtrList &list) {
if (list.size() < 2) {
MS_LOG(EXCEPTION) << "Input number " << list.size() << " of BoolEq is less than 2.";
constexpr size_t kListInputSize = 2;
if (list.size() != kListInputSize) {
MS_LOG(EXCEPTION) << "Input number of BoolEq must be 2, but got " << list.size();
}
ValuePtr x = list[0];
ValuePtr y = list[1];
@ -464,7 +469,7 @@ ValuePtr BoolEq(const ValuePtrList &list) {
return MakeValue(res);
}
MS_LOG(EXCEPTION) << "Unsported Value for BoolEq, x: " << x->ToString() << ".";
MS_LOG(EXCEPTION) << "Unsupported Value for BoolEq, x: " << x->ToString() << " y: " << y->ToString() << ".";
}
} // namespace prim
} // namespace mindspore

6
mindspore/ccsrc/frontend/operator/composite/composite.cc
View File

@ -131,7 +131,7 @@ AnfNodePtr HyperMap::FullMake(const std::shared_ptr<List> &type, const FuncGraph
return false;
});
if (is_not_same) {
MS_LOG(EXCEPTION) << "List in HyperMap should have same length";
MS_LOG(EXCEPTION) << "List in HyperMap should have same length.";
}
// cannot use shared_from_base() also known as this, as it will make a reference cycle on
@ -189,7 +189,7 @@ AnfNodePtr HyperMap::FullMake(const std::shared_ptr<Tuple> &type, const FuncGrap
return false;
});
if (is_not_same) {
MS_LOG(EXCEPTION) << "Tuple in HyperMap should have same length";
MS_LOG(EXCEPTION) << "Tuple in HyperMap should have same length.";
}
// cannot use shared_from_base() also known as this, as it will make a reference cycle on
@ -469,7 +469,7 @@ FuncGraphPtr Tail::GenerateFuncGraph(const AbstractBasePtrList &args_spec_list)
return GenerateSequeueFuncGraph(a->cast<abstract::AbstractSequeuePtr>());
}
MS_LOG(EXCEPTION) << "arg0 must be AbstractTuple or AbstractList, but: " << a->ToString();
MS_LOG(EXCEPTION) << "'Tail' arg0 must be AbstractTuple or AbstractList, but: " << a->ToString();
}
REGISTER_PYBIND_DEFINE(

4
mindspore/ccsrc/frontend/operator/composite/do_signature.cc
View File

@ -237,7 +237,7 @@ void DoAutoCast(const std::string &func_name, const std::vector<Signature> &sign
if ((arg_value->isa<TensorType>()) && arg_type_id == it->second) {
continue;
}
MS_LOG(DEBUG) << "do cast for inputs " << i << " " << (*op_inputs)[i + 1]->ToString() << " " << arg_type_id
MS_LOG(DEBUG) << "Do cast for inputs " << i << " " << (*op_inputs)[i + 1]->ToString() << " " << arg_type_id
<< " to " << it->second;
(*op_inputs)[i + 1] = DoCast((*op_inputs)[i + 1], it->second, graph);
}
@ -339,7 +339,7 @@ void RaiseExceptionForConvertRefDtype(const std::string &func_name, const std::s
const std::string &target_type) {
MS_LOG(EXCEPTION) << "In op '" << func_name << "', \n"
<< "the type of writable argument is '" << ref_type << "', "
<< "but the largest type in the same SignatureEumDtype is '" << target_type
<< "but the largest type in the same SignatureEnumDType is '" << target_type
<< "'. The writable arg type is not equal to the largest type, "
<< "so can not cast automatically.";
}

6
mindspore/ccsrc/frontend/operator/composite/map.cc
View File

@ -88,7 +88,7 @@ AnfNodePtr Map::FullMakeList(const std::shared_ptr<List> &type, const FuncGraphP
return false;
});
if (is_not_same) {
MS_LOG(EXCEPTION) << "List in Map should have same length";
MS_LOG(EXCEPTION) << "List in Map should have same length.";
}
constexpr size_t kPrimHoldLen = 1;
@ -147,7 +147,7 @@ AnfNodePtr Map::FullMakeTuple(const std::shared_ptr<Tuple> &type, const FuncGrap
return false;
});
if (is_not_same) {
MS_LOG(EXCEPTION) << "tuple in Map should have same length";
MS_LOG(EXCEPTION) << "Tuple in Map should have same length.";
}
constexpr size_t kPrimHoldLen = 1;
@ -227,7 +227,7 @@ AnfNodePtr Map::FullMakeClass(const std::shared_ptr<Class> &type, const FuncGrap
AnfNodePtr Map::Make(const FuncGraphPtr &func_graph, const AnfNodePtr &fn_arg, const ArgsPairList &arg_pairs) {
if (arg_pairs.empty()) {
MS_EXCEPTION(TypeError) << "map() must have at least two arguments";
MS_EXCEPTION(TypeError) << "The map operator must have at least two arguments.";
}
bool found = false;
TypeId id = kObjectTypeEnd;

2
mindspore/ccsrc/frontend/operator/composite/unpack_call.cc
View File

@ -85,7 +85,7 @@ FuncGraphPtr UnpackCall::GenerateFuncGraph(const AbstractBasePtrList &args_spec_
{NewValueNode(prim::kPrimMakeKeywordArg), NewValueNode(item.first), dict_get_item});
});
} else {
MS_LOG(EXCEPTION) << op_name << " require args should be tuple or dict, but got "
MS_LOG(EXCEPTION) << op_name << " require args should be tuple, list or dict, but got "
<< args_spec_list[index]->ToString();
}
}

6
mindspore/ccsrc/frontend/operator/composite/zip_operation.cc
View File

@ -43,12 +43,12 @@ FuncGraphPtr ZipOperation::GenerateFuncGraph(const AbstractBasePtrList &args_spe
MS_LOG(EXCEPTION) << "For 'zip', there is at least one input.";
}
auto is_all_sequeue =
auto all_is_sequence =
std::all_of(args_spec_list.begin(), args_spec_list.end(), [](const AbstractBasePtr &abs) -> bool {
MS_EXCEPTION_IF_NULL(abs);
return abs->isa<AbstractSequeue>();
});
if (!is_all_sequeue) {
if (!all_is_sequence) {
MS_LOG(EXCEPTION) << "For 'zip', all inputs must be sequence.";
}
@ -62,7 +62,7 @@ FuncGraphPtr ZipOperation::GenerateFuncGraph(const AbstractBasePtrList &args_spe
(void)ret_graph->add_parameter();
}
// generate tuple output of ziped arguments input
// generate tuple output of zipped arguments input
std::vector<AnfNodePtr> make_tuple_nodes;
make_tuple_nodes.push_back(NewValueNode(prim::kPrimMakeTuple));
for (size_t idx = 0; idx < (*min_abs)->cast<AbstractSequeuePtr>()->size(); idx++) {

2
mindspore/ccsrc/frontend/operator/ops_extends.cc
View File

@ -26,7 +26,7 @@ ValuePtr GetPythonOps(const std::string &op_name, const std::string &module_name
ValuePtr node = nullptr;
bool succ = parse::ConvertData(obj, &node, use_signature);
if (!succ) {
MS_LOG(EXCEPTION) << "get Python op " << op_name << " from " << module_name << " fail";
MS_LOG(EXCEPTION) << "Get Python op " << op_name << " from " << module_name << " fail.";
}
return node;
}

74
mindspore/ccsrc/frontend/operator/ops_front_infer_function.cc
View File

@ -64,7 +64,7 @@ void CalcSlidePara(const AbstractBasePtrList &args_spec_list, SlideInfo *slide)
MS_EXCEPTION_IF_NULL(args_spec_list[0]);
auto arg_value = args_spec_list[0]->BuildValue();
if (!arg_value->isa<Int64Imm>()) {
MS_LOG(EXCEPTION) << "Only supported input an int64 number.";
MS_LOG(EXCEPTION) << "The type of inputs of make_range operator only support int64 number.";
}
arg1 = GetValue<int64_t>(arg_value);
}
@ -73,7 +73,7 @@ void CalcSlidePara(const AbstractBasePtrList &args_spec_list, SlideInfo *slide)
MS_EXCEPTION_IF_NULL(args_spec_list[1]);
auto arg_value = args_spec_list[1]->BuildValue();
if (!arg_value->isa<Int64Imm>()) {
MS_LOG(EXCEPTION) << "Only supported input an int64 number.";
MS_LOG(EXCEPTION) << "The type of inputs of make_range operator only support int64 number.";
}
arg2 = GetValue<int64_t>(arg_value);
}
@ -82,7 +82,7 @@ void CalcSlidePara(const AbstractBasePtrList &args_spec_list, SlideInfo *slide)
MS_EXCEPTION_IF_NULL(args_spec_list[2]);
auto arg_value = args_spec_list[2]->BuildValue();
if (!arg_value->isa<Int64Imm>()) {
MS_LOG(EXCEPTION) << "Only supported input an int64 number.";
MS_LOG(EXCEPTION) << "The type of inputs of make_range operator only support int64 number.";
}
slide->step = GetValue<int64_t>(arg_value);
slide->start = arg1;
@ -183,11 +183,12 @@ AbstractBasePtr InferImplTypeof(const AnalysisEnginePtr &, const PrimitivePtr &,
AbstractBasePtr InferImplHasType(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
MS_EXCEPTION_IF_NULL(primitive);
// Inputs: a pointer to an AbstractBase object and a pointer to a Type
const std::string op_name = primitive->name();
CheckArgsSize(op_name, args_spec_list, 2);
AbstractTypePtr abs_type = CheckArg<AbstractType>(op_name, args_spec_list, 1);
MS_EXCEPTION_IF_NULL(abs_type);
auto mode_v = abs_type->GetValueTrack();
MS_EXCEPTION_IF_NULL(mode_v);
if (!mode_v->isa<Type>()) {
@ -229,7 +230,7 @@ AbstractBasePtr DoInferReduceShape(const AbstractTuplePtr &x_shape, const ValueP
int64_t e_value = CheckAxis(primitive->name(), elem, -SizeToLong(x_rank), SizeToLong(x_rank) - 1);
(void)axis_set.insert(e_value);
}
MS_EXCEPTION_IF_NULL(x_shp_value->cast<ValueTuplePtr>());
auto x_shp_data = x_shp_value->cast<ValueTuplePtr>()->value();
if (x_shp_data.size() < x_rank) {
MS_LOG(EXCEPTION) << "x_shape_data.size() " << x_shp_data.size() << " less than x_shape.size() " << x_rank;
@ -254,6 +255,7 @@ AbstractBasePtr InferImplBroadcastGradientArgs(const AnalysisEnginePtr &, const
// this primitive get the index that need to reduce
// input: x's shape and y's shape, inputs should be tuple
// output: tuple of x and y 's reduce index, reduce index should be a tuple
MS_EXCEPTION_IF_NULL(primitive);
const std::string op_name = primitive->name();
const size_t inputs_size = 2;
CheckArgsSize(op_name, args_spec_list, inputs_size);
@ -289,6 +291,7 @@ AbstractBasePtr InferImplListMap(const AnalysisEnginePtr &engine, const Primitiv
const AbstractBasePtrList &args_spec_list) {
// Inputs: fn, list1, list2, ...
MS_EXCEPTION_IF_NULL(engine);
MS_EXCEPTION_IF_NULL(primitive);
if (args_spec_list.size() <= 1) {
MS_LOG(EXCEPTION) << "List_map requires at least 1 list. while the input size is " << args_spec_list.size() << ".";
}
@ -317,11 +320,13 @@ AbstractBasePtr InferImplListReduce(const AnalysisEnginePtr &engine, const Primi
const AbstractBasePtrList &args_spec_list) {
// Inputs: a fn, a list and an object of a subclass of a AbstractBase.
MS_EXCEPTION_IF_NULL(engine);
MS_EXCEPTION_IF_NULL(primitive);
const std::string op_name = primitive->name();
const size_t inputs_size = 3;
CheckArgsSize(op_name, args_spec_list, inputs_size);
AbstractFunctionPtr fn = CheckArg<AbstractFunction>(op_name, args_spec_list, 0);
AbstractListPtr lst = CheckArg<AbstractList>(op_name, args_spec_list, 1);
MS_EXCEPTION_IF_NULL(lst);
AbstractBasePtr dflt = args_spec_list[2];
AbstractBasePtr list_type = AbstractJoin(lst->elements());
@ -337,10 +342,11 @@ AbstractBasePtr InferImplListReduce(const AnalysisEnginePtr &engine, const Primi
AbstractBasePtr InferImplTupleReversed(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
// Inputs: a tuple
MS_EXCEPTION_IF_NULL(primitive);
const std::string op_name = primitive->name();
CheckArgsSize(op_name, args_spec_list, 1);
AbstractTuplePtr input = CheckArg<AbstractTuple>(op_name, args_spec_list, 0);
MS_EXCEPTION_IF_NULL(input);
auto tuple_elements = input->elements();
AbstractBasePtrList elem_list;
(void)std::transform(tuple_elements.rbegin(), tuple_elements.rend(), std::back_inserter(elem_list),
@ -351,10 +357,12 @@ AbstractBasePtr InferImplTupleReversed(const AnalysisEnginePtr &, const Primitiv
AbstractBasePtr InferImplReduceShape(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
// Inputs: x_shape, axis
MS_EXCEPTION_IF_NULL(primitive);
const std::string op_name = primitive->name();
constexpr size_t arg_size = 2;
CheckArgsSize(op_name, args_spec_list, arg_size);
AbstractTuplePtr shape_x = CheckArg<AbstractTuple>(op_name, args_spec_list, 0);
MS_EXCEPTION_IF_NULL(shape_x);
MS_EXCEPTION_IF_NULL(args_spec_list[1]);
auto x_shp_value = shape_x->BuildValue();
@ -391,44 +399,48 @@ AbstractBasePtr InferImplReduceShape(const AnalysisEnginePtr &, const PrimitiveP
AbstractBasePtr InferImplTupleDiv(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
// Inputs: two tuples.
MS_EXCEPTION_IF_NULL(primitive);
const std::string op_name = primitive->name();
constexpr size_t arg_size = 2;
CheckArgsSize(op_name, args_spec_list, arg_size);
AbstractTuplePtr shape_x = CheckArg<AbstractTuple>(op_name, args_spec_list, 0);
AbstractTuplePtr div_shp = CheckArg<AbstractTuple>(op_name, args_spec_list, 1);
MS_LOG(INFO) << "DivShape input:" << shape_x->ToString() << ", div:" << div_shp->ToString();
MS_EXCEPTION_IF_NULL(shape_x);
MS_EXCEPTION_IF_NULL(div_shp);
MS_LOG(INFO) << "The shape of dividend:" << shape_x->ToString() << ", the shape of divisor:" << div_shp->ToString();
auto div_shp_value = div_shp->BuildValue();
if (div_shp_value->isa<AnyValue>()) {
MS_LOG(EXCEPTION) << "shape's data field can't be anythin: " << args_spec_list[0]->ToString();
MS_LOG(EXCEPTION) << "The shape's data field can't be anything: " << args_spec_list[0]->ToString();
}
auto shpx_value = shape_x->BuildValue();
if (shpx_value->isa<AnyValue>()) {
MS_LOG(EXCEPTION) << "shape's data field can't be anythin: " << args_spec_list[1]->ToString();
auto shape_x_value = shape_x->BuildValue();
if (shape_x_value->isa<AnyValue>()) {
MS_LOG(EXCEPTION) << "The shape's data field can't be anything: " << args_spec_list[1]->ToString();
}
if (div_shp->size() != shape_x->size()) {
MS_LOG(EXCEPTION) << "tileshape elems shape must the same div_shp: " << div_shp->size()
<< ", shapex: " << shape_x->size() << ".";
MS_LOG(EXCEPTION) << "The size of inputs of tuple_div operator must be same, but the size of divisor tuple is"
<< div_shp->size() << ", the size of dividend tuple is " << shape_x->size() << ".";
}
auto shpx_data = shpx_value->cast<ValueTuplePtr>()->value();
auto div_shp_data = div_shp_value->cast<ValueTuplePtr>()->value();
auto shape_x_data = shape_x_value->cast<ValueTuplePtr>()->value();
auto div_shape_data = div_shp_value->cast<ValueTuplePtr>()->value();
AbstractBasePtrList values;
for (size_t i = 0; i < div_shp_data.size(); i++) {
if (div_shp_data[i]->cast<Int64ImmPtr>() == nullptr) {
for (size_t i = 0; i < div_shape_data.size(); i++) {
if (div_shape_data[i]->cast<Int64ImmPtr>() == nullptr) {
MS_LOG(EXCEPTION) << "div_shp_shape data should be an int64 number, but it's " << args_spec_list[1]->ToString();
}
int64_t shapex_value = GetValue<int64_t>(shpx_data[i]);
int64_t div_value = GetValue<int64_t>(div_shp_data[i]);
int64_t shapex_value = GetValue<int64_t>(shape_x_data[i]);
int64_t div_value = GetValue<int64_t>(div_shape_data[i]);
MS_LOG(DEBUG) << "div_shp_shape data shapex_value :" << shapex_value << " div_value: " << div_value;
if (div_value == 0) {
MS_LOG(EXCEPTION) << "error: division value should not be 0!";
MS_LOG(EXCEPTION) << "The divisor value should not be 0!";
}
if ((shapex_value % div_value) != 0) {
MS_LOG(EXCEPTION) << "div_shp_shape data shapex must div int64_t:" << shapex_value << " div_value: " << div_value;
MS_LOG(EXCEPTION) << "The inputs of tuple_div is not divisible, the dividend is :" << shapex_value
<< ", the divisor is: " << div_value << ".";
}
int64_t result = shapex_value / div_value;
@ -445,13 +457,13 @@ AbstractBasePtr InferImplTuple2Array(const AnalysisEnginePtr &, const PrimitiveP
const std::string op_name = primitive->name();
CheckArgsSize(op_name, args_spec_list, 1);
AbstractTuplePtr input = CheckArg<AbstractTuple>(op_name, args_spec_list, 0);
MS_EXCEPTION_IF_NULL(input);
py::tuple data_tuple = ValueToPyData(input->BuildValue());
py::array data = py::array(data_tuple);
auto tensor = tensor::TensorPy::MakeTensor(data);
auto ret = tensor->ToAbstract();
ret->set_value(tensor);
MS_LOG(DEBUG) << "Tuple2arry result AbstractTensor: " << ret->ToString();
MS_LOG(DEBUG) << "Tuple2array result AbstractTensor: " << ret->ToString();
return ret;
}
@ -465,7 +477,7 @@ AbstractBasePtr InferImplShapeMul(const AnalysisEnginePtr &, const PrimitivePtr
auto shpx_value = shape_x->BuildValue();
if (shpx_value->isa<AnyValue>()) {
MS_LOG(EXCEPTION) << "shape's data field can't be anythin: " << shape_x->ToString();
MS_LOG(EXCEPTION) << "The shape's data field can't be anything: " << shape_x->ToString();
}
auto shpx_data = shpx_value->cast<ValueTuplePtr>()->value();
@ -477,7 +489,7 @@ AbstractBasePtr InferImplShapeMul(const AnalysisEnginePtr &, const PrimitivePtr
}
auto result_v = MakeValue(result);
MS_LOG(DEBUG) << "shape mul result:" << result_v->ToString();
MS_LOG(DEBUG) << "The result of shape_mul:" << result_v->ToString();
return std::make_shared<AbstractScalar>(result_v, result_v->type());
}
@ -523,8 +535,8 @@ AbstractBasePtr InferImplMakeSlice(const AnalysisEnginePtr &, const PrimitivePtr
ValuePtr scalar_index = MakeValue(static_cast<int64_t>(scalar_value->cast<BoolImmPtr>()->value()));
slice_args.push_back(scalar_index->ToAbstract());
} else {
MS_EXCEPTION(TypeError) << "MakeSlice eval " << index
<< " the input scalar type should be int or bool, but got " << scalar_value->ToString();
MS_EXCEPTION(TypeError) << "The " << index << "th input of scalar should be int or bool, but got "
<< scalar_value->ToString();
}
} else if (args_spec_list[index]->isa<AbstractTensor>()) {
auto arg = args_spec_list[index]->cast<AbstractTensorPtr>();
@ -552,7 +564,7 @@ AbstractBasePtr InferImplMakeSlice(const AnalysisEnginePtr &, const PrimitivePtr
slice_args.push_back(args_spec_list[index]);
}
} else {
MS_EXCEPTION(TypeError) << "MakeSlice eval " << index << " inputs should scalar, None or Tensor, but got"
MS_EXCEPTION(TypeError) << "The " << index << "th input of MakeSlice should be scalar, none or tensor, but got"
<< args_spec_list[index]->ToString();
}
}
@ -563,19 +575,19 @@ AbstractBasePtr InferImplMakeSlice(const AnalysisEnginePtr &, const PrimitivePtr
AbstractBasePtr InferImplMakeRange(const AnalysisEnginePtr &, const PrimitivePtr &,
const AbstractBasePtrList &args_spec_list) {
if (args_spec_list.empty()) {
MS_LOG(EXCEPTION) << "Cannot make range from empty input.";
MS_LOG(EXCEPTION) << "The inputs of make_range operator could not be empty.";
}
constexpr size_t max_args_size = 3;
if (args_spec_list.size() > max_args_size) {
MS_LOG(EXCEPTION) << "Error args size of make range operational.";
MS_LOG(EXCEPTION) << "The size of inputs of make_range operator could not exceed 3.";
}
SlideInfo slide = {0, 1, 0};
CalcSlidePara(args_spec_list, &slide);
if (slide.step == 0) {
MS_LOG(EXCEPTION) << "Error, step value is 0.";
MS_LOG(EXCEPTION) << "The step value of make_range operator could not be 0.";
}
AbstractBasePtrList args;

2
tests/st/ops/ascend/test_dynamic_broadcast_gradient_args.py
View File

@ -25,7 +25,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.args = _inner_ops.BroadcastGradientArgs()
self.args = _inner_ops.DynamicBroadcastGradientArgs()
def construct(self, s0, s1):
return self.args(s0, s1)

189
tests/syntax/simple_expression/test_hypermap.py Normal file
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@ -0,0 +1,189 @@
# Copyright 2021 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.
# ============================================================================
import pytest
import numpy as np
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore.nn import Cell
add = P.Add()
hyper_map = C.HyperMap()
@ms_function
def main_noleaf(x, y):
return hyper_map(add, x, y)
def test_hypermap_noleaf_tuple_list_mix():
"""
Feature: Check the types of inputs of HyperMap.
Description: The types of inputs of HyperMap must be the same.
Expectation: The types of inputs of HyperMap must be the same.
"""
tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
tensor2 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
with pytest.raises(Exception, match="HyperMap cannot match up all input types of arguments."):
main_noleaf((tensor1, 1), [tensor2, 2])
def test_hypermap_noleaf_tuple_length():
"""
Feature: Check the length of arg of Tuple in HyperMap.
Description: The length of inputs of HyperMap must be the same.
Expectation: The length of inputs of HyperMap must be the same.
"""
tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
tensor2 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
with pytest.raises(Exception, match="Tuple in HyperMap should have same length."):
main_noleaf((tensor1, 1), (tensor2, 2, 2))
def test_hypermap_noleaf_list_length():
"""
Feature: Check the length of arg of List in HyperMap.
Description: Check the length of arg of List in HyperMap.
Expectation: Check the length of arg of List in HyperMap.
"""
tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
tensor2 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
with pytest.raises(Exception, match="List in HyperMap should have same length."):
main_noleaf([tensor1], [tensor2, tensor2])
def test_hypermap_noleaf_list_tuple():
"""
Feature: Check the types of inputs of HyperMap.
Description: The types of inputs of HyperMap must be the same.
Expectation: The types of inputs of HyperMap must be the same.
"""
tensor1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
tensor2 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]).astype('float32'))
with pytest.raises(Exception, match="HyperMap cannot match up all input types of arguments."):
main_noleaf([tensor1], (tensor2, tensor2))
def test_tuple_slice_stop_index():
"""
Feature: Check the type of stop index of slice.
Description: The type of stop index of slice must be scalar, None or Tensor.
Expectation: The type of stop index of slice must be scalar, None or Tensor.
"""
class TupleSliceNet(Cell):
def __init__(self):
super(TupleSliceNet, self).__init__()
self.addN = P.AddN()
self.index_0 = Tensor(3)
def construct(self, tensor_tuple):
tensor_tuple_slice0 = tensor_tuple[:]
tensor_tuple_slice1 = tensor_tuple[self.index_0:"str"] # slice should be Scalar or None, rather than string
sum0 = self.addN(tensor_tuple_slice0)
sum1 = self.addN(tensor_tuple_slice1)
ret = sum0 + sum1
return ret
data = (Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))
net = TupleSliceNet()
with pytest.raises(Exception, match="The 1th input of MakeSlice should be scalar, none or tensor, but got str"):
output = net(data)
print("output:", output)
def test_tuple_slice_start_index():
"""
Feature: Check the type of start index of slice.
Description: The type of start index of slice must be scalar, None or Tensor.
Expectation: The type of start index of slice must be scalar, None or Tensor.
"""
class TupleSliceNet(Cell):
def __init__(self):
super(TupleSliceNet, self).__init__()
self.addN = P.AddN()
self.index_0 = Tensor(3)
self.index_1 = Tensor([5])
self.index_3 = Tensor([True])
def construct(self, tensor_tuple):
tensor_tuple_slice0 = tensor_tuple[:]
tensor_tuple_slice1 = tensor_tuple["str":self.index_0]
tensor_tuple_slice2 = tensor_tuple[self.index_3:]
tensor_tuple_slice3 = tensor_tuple[2:self.index_1:]
sum0 = self.addN(tensor_tuple_slice0)
sum1 = self.addN(tensor_tuple_slice1)
sum2 = self.addN(tensor_tuple_slice2)
sum3 = self.addN(tensor_tuple_slice3)
ret = sum0 + sum1 + sum2 + sum3
return ret
data = (Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))
net = TupleSliceNet()
with pytest.raises(Exception, match="The 0th input of MakeSlice should be scalar, none or tensor, but got str"):
output = net(data)
print("output:", output)
def test_tuple_slice_step():
"""
Feature: Check the type of step of slice.
Description: The type of step of slice must not be 0.
Expectation: The type of step of slice must be scalar, None or Tensor.
"""
class TupleSliceNet(Cell):
def __init__(self):
super(TupleSliceNet, self).__init__()
self.addN = P.AddN()
self.index_0 = Tensor(3)
self.index_1 = Tensor([5])
self.index_3 = Tensor([True])
def construct(self, tensor_tuple):
tensor_tuple_slice0 = tensor_tuple[:]
tensor_tuple_slice1 = tensor_tuple[:self.index_0]
tensor_tuple_slice2 = tensor_tuple[self.index_3:]
tensor_tuple_slice3 = tensor_tuple[2:self.index_1:0]
sum0 = self.addN(tensor_tuple_slice0)
sum1 = self.addN(tensor_tuple_slice1)
sum2 = self.addN(tensor_tuple_slice2)
sum3 = self.addN(tensor_tuple_slice3)
ret = sum0 + sum1 + sum2 + sum3
return ret
data = (Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))
net = TupleSliceNet()
with pytest.raises(Exception, match="TupleSlice require the step value could not be 0, but got 0."):
output = net(data)
print("output:", output)

170
tests/syntax/simple_expression/test_map.py Normal file
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@ -0,0 +1,170 @@
# Copyright 2021 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.
# ============================================================================
import pytest
import numpy as np
from mindspore import Tensor, nn, Parameter
from mindspore.nn import Cell
import mindspore as ms
def test_map_args_size():
"""
Feature: Check the size of inputs of map.
Description: The size of inputs of map must be greater than 1.
Expectation: The size of inputs of map must be greater than 1.
"""
class MapNet(Cell):
def __init__(self):
super().__init__()
self.relu = nn.ReLU()
def mul(self, x=2, y=4):
return x * y
def construct(self, x):
if map(self.mul) == 8:
x = self.relu(x)
return x
input_np_x = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_me_x = Tensor(input_np_x)
net = MapNet()
with pytest.raises(Exception, match="The map operator must have at least two arguments."):
ret = net(input_me_x)
print("ret:", ret)
def test_map_args_type():
"""
Feature: Check the type of inputs of Map().
Description: The type of inputs of Map() must be list, tuple or class.
Expectation: The type of inputs of Map() must be list, tuple or class.
"""
class MapNet(Cell):
def __init__(self):
super().__init__()
self.relu = nn.ReLU()
def mul(self, x=2, y=4):
return x * y
def construct(self, x):
if map(self.mul, 3, 4) == 8:
x = self.relu(x)
return x
input_np_x = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_me_x = Tensor(input_np_x)
net = MapNet()
with pytest.raises(Exception, match="Map can only be applied to list, tuple and class"):
ret = net(input_me_x)
print("ret:", ret)
def test_map_args_full_make_list():
"""
Feature: Check the types of all inputs in Map.
Description: The types of all inputs in Map must be same.
Expectation: The types of all inputs in Map must be same.
"""
class MapNet(Cell):
def mul(self, x=2, y=4):
return x * y
def construct(self, x, y):
if map(self.mul, x, y) == [8]:
x = y
return x
input_me_x = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
input_me_y = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
net = MapNet()
with pytest.raises(Exception, match="Map cannot match up all input types of arguments."):
ret = net([input_me_x], (input_me_y))
print("ret:", ret)
def test_map_args_full_make_list_same_length():
"""
Feature: Check the length of list input Map.
Description: The list in Map should have same length.
Expectation: The list in Map should have same length.
"""
class MapNet(Cell):
def mul(self, x=2, y=4):
return x * y
def construct(self, x, y):
if map(self.mul, x, y) == [8]:
x = y
return x
input_me_x = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
input_me_y = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
net = MapNet()
with pytest.raises(Exception, match="List in Map should have same length."):
ret = net([input_me_x], [input_me_y, input_me_y])
print("ret:", ret)
def test_map_args_full_make_tuple_same_length():
"""
Feature: Check the length of tuple input Map.
Description: The tuple in Map should have same length.
Expectation: The tuple in Map should have same length.
"""
class MapNet(Cell):
def mul(self, x=2, y=4):
return x * y
def construct(self, x, y):
if map(self.mul, x, y) == [8]:
x = y
return x
input_me_x = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
input_me_y = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
net = MapNet()
with pytest.raises(Exception, match="Tuple in Map should have same length."):
ret = net((input_me_x, input_me_x), (input_me_y, input_me_y, input_me_y))
print("ret:", ret)
def test_map_param_cast():
"""
Feature: Check the ref type when insert auto cast.
Description: Check the ref type when insert auto cast.
Expectation: Check the ref type when insert auto cast.
"""
class MapNet(Cell):
def __init__(self):
super().__init__()
self.param = Parameter(Tensor(5, ms.float32), name="param_b")
def construct(self, x):
self.param = x
return self.param
input_me_x = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float64))
net = MapNet()
with pytest.raises(Exception, match="In op 'S-Prim-Assign', the type of writable argument is 'float32'"):
ret = net(input_me_x)
print("ret:", ret)

240
tests/syntax/simple_expression/test_operator.py Normal file
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@ -0,0 +1,240 @@
# Copyright 2021 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.
# ============================================================================
import sys
import pytest
from mindspore import Tensor, context, Parameter
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.nn import Cell
import mindspore as ms
def test_inner_scalar_divisor():
"""
Feature: Check whether the divisor of inner scalar is zero.
Description: The divisor of inner scalar must not be zero.
Expectation: The divisor of inner scalar must not be zero.
"""
class Net(Cell):
def __init__(self):
super().__init__()
self.param_a = Parameter(Tensor(5, ms.int32), name="param_a")
self.param_b = Parameter(Tensor(5, ms.int32), name="param_b")
def construct(self, x):
return x + self.param_a + 5 / 0
context.set_context(device_target="GPU")
x = Tensor(2, dtype=ms.int32)
net = Net()
with pytest.raises(Exception, match="The divisor could not be zero."):
ret = net(x)
print("ret:", ret)
def test_inner_scalar_mod():
"""
Feature: Check the input of inner scalar mod.
Description: The input of inner scalar mod must not be zero.
Expectation: The input of inner scalar mod must not be zero.
"""
class Net(Cell):
def __init__(self):
super().__init__()
self.param_a = Parameter(Tensor(5, ms.int32), name="param_a")
def construct(self, x):
return x + self.param_a + 5 % 0
x = Tensor(2, dtype=ms.int32)
net = Net()
with pytest.raises(Exception, match="Could not mod to zero."):
ret = net(x)
print("ret:", ret)
def test_inner_scalar_mod_args_length():
"""
Feature: Check the length of input of inner scalar mod.
Description: The length of input of inner scalar mod should not less than 2.
Expectation: The length of input of inner scalar mod should not less than 2.
"""
class Net(Cell):
def __init__(self):
super().__init__()
self.param_a = Parameter(Tensor(5, ms.int32), name="param_a")
self.mod = P.Mod()
def construct(self, x):
return x + self.param_a + self.mod(5)
x = Tensor(2, dtype=ms.int32)
net = Net()
with pytest.raises(Exception, match="Function S-Prim-Mod's input length is not equal to Signature length."):
ret = net(x)
print("ret:", ret)
def test_make_range_input_is_empty():
"""
Feature: Check the length of inputs of make_range operator.
Description: The inputs of make_range operator could not be empty.
Expectation: The inputs of make_range operator could not be empty.
"""
class Net(Cell):
def construct(self, x, y):
for _ in F.make_range():
x += y
return x
x = Tensor(2, dtype=ms.int32)
y = Tensor(4, dtype=ms.int32)
net = Net()
with pytest.raises(Exception, match="The inputs of make_range operator could not be empty."):
ret = net(x, y)
print("ret:", ret)
def test_make_range_input_type():
"""
Feature: Check the type of inputs of make_range operator.
Description: The type of inputs of make_range operator must be int64.
Expectation: The type of inputs of make_range operator must be int64.
"""
class Net(Cell):
def construct(self, x, y):
for _ in F.make_range(0, 0.02):
x += y
return x
x = Tensor(2, dtype=ms.int32)
y = Tensor(4, dtype=ms.int32)
net = Net()
with pytest.raises(Exception, match="The type of inputs of make_range operator only support int64 number."):
ret = net(x, y)
print("ret:", ret)
def test_make_range_input_size():
"""
Feature: Check the size of inputs of make_range operator.
Description: The size of inputs of make_range operator could not exceed 3.
Expectation: The size of inputs of make_range operator could not exceed 3.
"""
class Net(Cell):
def construct(self, x, y):
for _ in F.make_range(1, 2, 3, 4):
x += y
return x
x = Tensor(2, dtype=ms.int32)
y = Tensor(4, dtype=ms.int32)
net = Net()
with pytest.raises(Exception, match="The size of inputs of make_range operator could not exceed 3."):
ret = net(x, y)
print("ret:", ret)
def test_make_range_overflow():
"""
Feature: Check the size of inputs of make_range operator.
Description: The size of inputs of make_range operator could not exceed 3.
Expectation: The size of inputs of make_range operator could not exceed 3.
"""
class Net(Cell):
def construct(self, x, y):
max_index = sys.maxsize
for _ in F.make_range(max_index - 1, max_index, 3):
x += y
return x
x = Tensor(2, dtype=ms.int32)
y = Tensor(4, dtype=ms.int32)
net = Net()
with pytest.raises(Exception, match="For make range, the required cycles number is greater than max cycles number"):
ret = net(x, y)
print("ret:", ret)
def test_typeof():
"""
Feature: Check the size of inputs of typeof operator.
Description: The size of inputs of typeof operator must be 1.
Expectation: The size of inputs of typeof operator must be 1.
"""
class Net(Cell):
def construct(self, x):
return F.typeof(x, x)
x = Tensor([2, 3, 4, 5], dtype=ms.int32)
net = Net()
with pytest.raises(Exception, match="Typeof evaluator requires 1 parameter, while the input size is 2."):
ret = net(x)
print("ret:", ret)
def test_tuple_div():
"""
Feature: Check the size of inputs of tuple_div operator.
Description: The size of inputs of tuple_div operator must be same.
Expectation: The size of inputs of tuple_div operator must be same.
"""
class Net(Cell):
def construct(self, x, y):
return F.tuple_div(x, y)
x = (8, 14, 20)
y = (2, 2)
net = Net()
with pytest.raises(Exception, match="The size of inputs of tuple_div operator must be same"):
ret = net(x, y)
print("ret:", ret)
def test_tuple_div_input_is_not_divisible():
"""
Feature: Check whether the inputs of tuple_div is divisible.
Description: The inputs of tuple_div could be divisible.
Expectation: The inputs of tuple_div could be divisible.
"""
class Net(Cell):
def construct(self, x, y):
return F.tuple_div(x, y)
x = (8, 14)
y = (2, 3)
net = Net()
with pytest.raises(Exception, match="The inputs of tuple_div is not divisible"):
ret = net(x, y)
print("ret:", ret)
def test_make_slice_scalar():
"""
Feature: Check whether the scalar input of make_slice is int or bool.
Description: The scalar input of make_slice is int or bool.
Expectation: The scalar input of make_slice is int or bool.
"""
class Net(Cell):
def construct(self, data):
return data[F.make_slice(1.01, None, None)]
x = Tensor((8, 10, 12), dtype=ms.int32)
net = Net()
with pytest.raises(Exception, match="The 0th input of scalar should be int or bool"):
ret = net(x)
print("ret:", ret)

85
tests/syntax/simple_expression/test_zip_operation.py Normal file
View File

@ -0,0 +1,85 @@
# Copyright 2021 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.
# ============================================================================
import pytest
import numpy as np
from mindspore import Tensor, Parameter
from mindspore.ops import operations as P
from mindspore.nn import Cell
import mindspore as ms
def test_zip_operation_args_size():
"""
Feature: Check the size of inputs of ZipOperation.
Description: The inputs of ZipOperation must not be empty.
Expectation: The size of inputs of ZipOperation must be greater than 0.
"""
class AssignInZipLoop(Cell):
def __init__(self):
super().__init__()
self.conv1 = ms.nn.Conv2d(3, 2, 1, weight_init="zero")
self.conv2 = ms.nn.Conv2d(3, 2, 1, weight_init="zero")
self.params1 = self.conv1.trainable_params()
self.params2 = self.conv2.trainable_params()
def construct(self, x):
for p1, p2 in zip():
P.Assign()(p2, p1 + x)
out = 0
for p1, p2 in zip(self.params1, self.params2):
out = p1 + p2
return out
x = Tensor.from_numpy(np.ones([1], np.float32))
net = AssignInZipLoop()
with pytest.raises(Exception, match="For 'zip', there is at least one input."):
out = net(x)
assert np.all(out.asnumpy() == 1)
def test_zip_operation_args_type():
"""
Feature: Check the type of inputs of ZipOperation.
Description: Check whether all inputs in zip is sequeue.
Expectation: All inputs in zip must be sequeue.
"""
class AssignInZipLoop(Cell):
def __init__(self):
super().__init__()
self.conv1 = ms.nn.Conv2d(3, 2, 1, weight_init="zero")
self.conv2 = ms.nn.Conv2d(3, 2, 1, weight_init="zero")
self.params1 = self.conv1.trainable_params()
self.params2 = self.conv2.trainable_params()
self.param = Parameter(Tensor(5, ms.float32), name="param")
def construct(self, x):
for p1, p2 in zip(self.params1, self.params2, self.param):
P.Assign()(p2, p1 + x)
out = 0
for p1, p2 in zip(self.params1, self.params2):
out = p1 + p2
return out
x = Tensor.from_numpy(np.ones([1], np.float32))
net = AssignInZipLoop()
with pytest.raises(Exception, match="For 'zip', all inputs must be sequence."):
out = net(x)
assert np.all(out.asnumpy() == 1)

2
tests/ut/cpp/operator/cc_implementations_test.cc
View File

@ -191,7 +191,7 @@ TEST_F(TestImplementations, ScalarDivTest) {
ScalarDiv(list);
FAIL();
} catch (std::runtime_error const &err) {
ASSERT_TRUE(std::string(err.what()).find("Divisor could not be zero") != std::string::npos);
ASSERT_TRUE(std::string(err.what()).find("The divisor could not be zero.") != std::string::npos);
}
list.clear();