!49916 Add AnyTypeNode and NoneTypeNode
Merge pull request !49916 from NaCN/add_async_node
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Gitee
commit
f58509ff73
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@ -93,6 +93,25 @@ class SequenceNode : public StubNode {
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};
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using SequenceNodePtr = std::shared_ptr<SequenceNode>;
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class AnyTypeNode : public StubNode {
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public:
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AnyTypeNode() = default;
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MS_DECLARE_PARENT(AnyTypeNode, StubNode);
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bool SetAbstract(const AbstractBasePtr &abs) override;
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void SetValue(const ValuePtr &val) override;
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py::object GetRealNode();
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private:
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StubNodePtr real_node_;
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};
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class NoneTypeNode : public StubNode {
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public:
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NoneTypeNode() = default;
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MS_DECLARE_PARENT(NoneTypeNode, StubNode);
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py::object GetRealValue();
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};
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COMMON_EXPORT std::pair<py::object, StubNodePtr> MakeTopNode(const TypePtr &type);
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COMMON_EXPORT void RegStubNodes(const py::module *m);
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} // namespace stub
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@ -39,6 +39,8 @@ const TypePtr kTupleTensor8 = std::make_shared<Tuple>(
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const TypePtr kTupleTensor9 = std::make_shared<Tuple>(TypePtrList{
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kTensorType, kTensorType, kTensorType, kTensorType, kTensorType, kTensorType, kTensorType, kTensorType, kTensorType});
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// If Abstract of the operator is constant, please add the type kTypeNone, such as "Size".
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// If Abstract of the operator is Tensor and Tuple[Tensor], please add the type kAnyType, such as "Eigh".
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inline static PredictOutTypeMap out_type_prediction = {{"ActsULQ", kTupleTensor4},
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{"Adam", kTupleTensor3},
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{"AdamApplyOne", kTupleTensor3},
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@ -85,7 +87,7 @@ inline static PredictOutTypeMap out_type_prediction = {{"ActsULQ", kTupleTensor4
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{"CTCLoss", kTupleTensor2},
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{"CTCLossV2", kTupleTensor2},
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{"Coalesce", kTupleTensor3},
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{"ConcatOffset", kAnyType},
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{"ConcatOffset", kTypeNone},
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{"CombinedNonMaxSuppression", kTupleTensor4},
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{"ComputeAccidentalHits", kTupleTensor3},
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{"ConfusionMulGrad", kTupleTensor2},
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@ -129,7 +131,7 @@ inline static PredictOutTypeMap out_type_prediction = {{"ActsULQ", kTupleTensor4
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{"GRUV2HiddenGrad", kTupleTensor3},
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{"GRUV2HiddenGradCell", kTupleTensor3},
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{"Geqrf", kTupleTensor2},
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{"GetNext", kAnyType},
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{"GetNext", kTuple},
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{"GridSampler2DGrad", kTupleTensor2},
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{"GridSampler3D", kTupleTensor2},
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{"GridSampler3DGrad", kTupleTensor2},
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@ -138,7 +140,7 @@ inline static PredictOutTypeMap out_type_prediction = {{"ActsULQ", kTupleTensor4
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{"InstanceNormGrad", kTupleTensor3},
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{"InstanceNormV2", kTupleTensor3},
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{"InstanceNormV2Grad", kTupleTensor3},
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{"InvertPermutation", kAnyType},
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{"InvertPermutation", kTypeNone},
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{"LSTM", kTupleTensor5},
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{"LSTMGrad", kTupleTensor4},
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{"LSTMGradData", kTupleTensor3},
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@ -162,7 +164,7 @@ inline static PredictOutTypeMap out_type_prediction = {{"ActsULQ", kTupleTensor4
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{"Lu", kTupleTensor2},
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{"LuUnpack", kTupleTensor3},
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{"LuUnpackGrad", kTupleTensor2},
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{"MakeTuple", kAnyType},
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{"MakeTuple", kTypeNone},
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{"MapCacheIdx", kTupleTensor4},
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{"MapTensorGetData", kTupleTensor2},
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{"MatmulDDS", kTupleTensor2},
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@ -193,26 +195,26 @@ inline static PredictOutTypeMap out_type_prediction = {{"ActsULQ", kTupleTensor4
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{"ReduceStd", kTupleTensor2},
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{"ReservoirReplayBufferDestroy", kTupleTensor4},
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{"SampleDistortedBoundingBoxV2", kTupleTensor3},
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{"ScalarAdd", kAnyType},
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{"ScalarBool", kAnyType},
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{"ScalarDiv", kAnyType},
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{"ScalarFloordiv", kAnyType},
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{"ScalarMod", kAnyType},
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{"ScalarMul", kAnyType},
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{"ScalarSub", kAnyType},
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{"ScalarAdd", kTypeNone},
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{"ScalarBool", kTypeNone},
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{"ScalarDiv", kTypeNone},
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{"ScalarFloordiv", kTypeNone},
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{"ScalarMod", kTypeNone},
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{"ScalarMul", kTypeNone},
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{"ScalarSub", kTypeNone},
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{"SelfAdjointEig", kTupleTensor2},
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{"SequenceAdd", kAnyType},
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{"SequenceAddN", kAnyType},
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{"SequenceCount", kAnyType},
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{"SequenceIndex", kAnyType},
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{"SequenceMul", kAnyType},
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{"SequenceMax", kAnyType},
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{"SequenceMin", kAnyType},
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{"SequenceSlice", kAnyType},
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{"SequenceSliceGrad", kAnyType},
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{"SequenceSliceSetItem", kAnyType},
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{"SequenceZerosLike", kAnyType},
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{"Size", kAnyType},
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{"SequenceAdd", kTypeNone},
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{"SequenceAddN", kTypeNone},
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{"SequenceCount", kTypeNone},
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{"SequenceIndex", kTypeNone},
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{"SequenceMul", kTypeNone},
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{"SequenceMax", kTypeNone},
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{"SequenceMin", kTypeNone},
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{"SequenceSlice", kTypeNone},
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{"SequenceSliceGrad", kTypeNone},
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{"SequenceSliceSetItem", kTypeNone},
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{"SequenceZerosLike", kTypeNone},
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{"Size", kTypeNone},
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{"SoftmaxCrossEntropyWithLogits", kTupleTensor2},
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{"SoftmaxV2WithDropoutDoMaskV3", kTupleTensor2},
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{"Sort", kTupleTensor2},
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@ -249,27 +251,27 @@ inline static PredictOutTypeMap out_type_prediction = {{"ActsULQ", kTupleTensor4
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{"Sspaddmm", kTupleTensor3},
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{"SubAndFilter", kTupleTensor2},
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{"Svd", kTupleTensor3},
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{"TensorToList", kAnyType},
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{"TensorToScalar", kAnyType},
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{"TensorToTuple", kAnyType},
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{"TensorToList", kTypeNone},
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{"TensorToScalar", kTypeNone},
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{"TensorToTuple", kTypeNone},
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{"TopK", kTupleTensor2},
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{"TupleGetItem", kAnyType},
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{"TupleGetItem", kTypeNone},
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{"UniformCandidateSampler", kTupleTensor3},
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{"Unique", kTupleTensor2},
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{"UniqueConsecutive", kTupleTensor3},
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{"UniqueWithPad", kTupleTensor2},
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{"Unpack", kTuple},
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{"Unstack", kTuple},
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{"bit_and", kAnyType},
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{"bit_or", kAnyType},
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{"make_range", kAnyType},
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{"scalar_eq", kAnyType},
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{"scalar_ge", kAnyType},
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{"scalar_gt", kAnyType},
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{"scalar_le", kAnyType},
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{"scalar_lt", kAnyType},
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{"sequence_len", kAnyType},
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{"tuple_setitem", kAnyType}};
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{"bit_and", kTypeNone},
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{"bit_or", kTypeNone},
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{"make_range", kTypeNone},
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{"scalar_eq", kTypeNone},
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{"scalar_ge", kTypeNone},
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{"scalar_gt", kTypeNone},
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{"scalar_le", kTypeNone},
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{"scalar_lt", kTypeNone},
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{"sequence_len", kTypeNone},
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{"tuple_setitem", kTypeNone}};
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} // namespace pynative
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_PIPELINE_PYNATIVE_PREDICTOUTTYPEMAP_H_
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@ -79,7 +79,7 @@ TypePtr PredictOutTypeByName(const std::string &op_name) {
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}
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static auto operator_fns = ops::OperatorRegister::GetInstance().GetOperatorMap();
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if (operator_fns.find(op_name) == operator_fns.end()) {
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return ops_map[op_name] = kAnyType;
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return ops_map[op_name] = kTypeNone;
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}
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const auto pre_iter = out_type_prediction.find(op_name);
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auto type = pre_iter == out_type_prediction.end() ? kTensorType : pre_iter->second;
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@ -118,8 +118,8 @@ py::object PyNativeExecutor::RunOpAsync(const py::args &args) const {
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// 1. get top_type from Primitive::PredictOutputType
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auto top_type = PredictOutTypeByName(adapter->name());
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// 2. if predict failed(kAnyType), return after infer(half-asynchronous) or run(synchronous mode)
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if (top_type == kAnyType || DisablePyTraceAsync(op_run_info)) {
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// 2. if disable PyTraceAsync, return after infer(half-asynchronous) or run(synchronous mode)
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if (DisablePyTraceAsync(op_run_info)) {
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// Wait for async task finish
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forward_executor()->WaitForwardTask();
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PyNativeAlgo::Common::StubNodeToValue(op_run_info);
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@ -43,6 +43,10 @@ StubNodePtr MakeStubNode(const TypePtr &type) {
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node->SetElement(i, elem);
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}
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return node;
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} else if (type == kAnyType) {
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return std::make_shared<AnyTypeNode>();
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} else if (type == kTypeNone) {
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return std::make_shared<NoneTypeNode>();
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} else {
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if (!type->isa<TensorType>()) {
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MS_LOG(WARNING) << "stub tensor is create for type: " << type->ToString();
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@ -56,7 +60,7 @@ py::object MakeOutput(StubNodePtr node) {
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if (node->isa<TensorNode>()) {
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auto tensor = node->cast<std::shared_ptr<TensorNode>>();
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return py::cast(tensor);
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} else {
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} else if (node->isa<SequenceNode>()) {
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auto seq = node->cast<std::shared_ptr<SequenceNode>>();
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MS_EXCEPTION_IF_NULL(seq);
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auto &elements = seq->Elements();
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@ -68,6 +72,12 @@ py::object MakeOutput(StubNodePtr node) {
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out[i] = MakeOutput(elements[i]);
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}
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return out;
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} else if (node->isa<AnyTypeNode>()) {
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auto tensor = node->cast<std::shared_ptr<AnyTypeNode>>();
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return py::cast(tensor);
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} else {
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auto tensor = node->cast<std::shared_ptr<NoneTypeNode>>();
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return py::cast(tensor);
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}
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}
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} // namespace
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@ -225,6 +235,28 @@ void SequenceNode::SetValue(const ValuePtr &val) {
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StubNode::SetValue(val);
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}
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bool AnyTypeNode::SetAbstract(const AbstractBasePtr &abs) {
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real_node_ = MakeStubNode(abs->BuildType());
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auto flag = real_node_->SetAbstract(abs);
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(void)StubNode::SetAbstract(abs);
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return flag;
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}
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void AnyTypeNode::SetValue(const ValuePtr &val) {
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real_node_->SetValue(val);
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StubNode::SetValue(val);
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}
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py::object AnyTypeNode::GetRealNode() {
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WaitAbstract();
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return py::cast(real_node_);
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}
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py::object NoneTypeNode::GetRealValue() {
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auto val = WaitValue();
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return ValueToPyData(val);
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}
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std::pair<py::object, StubNodePtr> MakeTopNode(const TypePtr &type) {
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auto top = MakeStubNode(type);
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auto ret = MakeOutput(top);
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@ -239,6 +271,10 @@ void RegStubNodes(const py::module *m) {
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.def("get_dtype", &TensorNode::GetDtype, "get output dtype of async stub.");
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(void)py::class_<SequenceNode, StubNode, std::shared_ptr<SequenceNode>>(*m, "SequenceNode")
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.def("get_elements", &SequenceNode::GetElements, "get the elements of async stub_seq.");
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(void)py::class_<AnyTypeNode, StubNode, std::shared_ptr<AnyTypeNode>>(*m, "AnyTypeNode")
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.def("get_real_node", &AnyTypeNode::GetRealNode, "get the real StubNode");
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(void)py::class_<NoneTypeNode, StubNode, std::shared_ptr<NoneTypeNode>>(*m, "NoneTypeNode")
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.def("get_real_value", &NoneTypeNode::GetRealValue, "get the real value");
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}
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} // namespace stub
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} // namespace mindspore
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@ -18,7 +18,7 @@ import inspect
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from functools import reduce
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from mindspore.common.tensor import Tensor
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from mindspore.common.dtype import type_size_in_bytes
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from mindspore._c_expression import TensorNode, SequenceNode
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from mindspore._c_expression import TensorNode, SequenceNode, NoneTypeNode, AnyTypeNode
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from mindspore.common.api import _convert_python_data
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@ -163,6 +163,7 @@ _init_stub_tensor_api()
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def _convert_stub(stub):
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"convert stub to StubNode or Value"
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if isinstance(stub, TensorNode):
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return StubTensor(stub)
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if isinstance(stub, tuple):
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@ -170,4 +171,10 @@ def _convert_stub(stub):
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if isinstance(stub, SequenceNode):
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elements = stub.get_elements()
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return tuple(_convert_stub(e) for e in elements)
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if isinstance(stub, NoneTypeNode):
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val = stub.get_real_value()
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return _convert_python_data(val)
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if isinstance(stub, AnyTypeNode):
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val = stub.get_real_node()
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return _convert_stub(val)
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return _convert_python_data(stub)
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@ -23,6 +23,8 @@ import mindspore.nn as nn
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import mindspore.ops as P
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from mindspore import Tensor
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from mindspore.ops.operations import nn_ops as G
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from mindspore.common.api import _pynative_executor
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DEVICE_TARGET = "CPU"
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@ -345,6 +347,7 @@ def test_ps_roi_pooling_input_args_num():
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ms.Tensor(rois, dtype=ms.float32),
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ms.Tensor(features, dtype=ms.float32)
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)
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_pynative_executor.sync()
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except TypeError:
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return
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else:
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@ -22,6 +22,8 @@ import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops.operations import nn_ops as G
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from mindspore.common.api import _pynative_executor
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DEVICE_TARGET = "GPU"
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CTX_MODE = ms.context.GRAPH_MODE
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@ -387,6 +389,7 @@ def test_ps_roi_pooling_input_args_num():
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ms.Tensor(rois, dtype=ms.float32),
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ms.Tensor(features, dtype=ms.float32)
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)
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_pynative_executor.sync()
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except TypeError:
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return
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else:
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