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Scott McKay 2025-01-18 11:22:58 +10:00
parent a52e268613
commit e915f01b41
4 changed files with 84 additions and 16 deletions
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20
include/onnxruntime/core/session/onnxruntime_c_api.h
View file

@ -5282,14 +5282,14 @@ struct OrtModelBuilderApi {
/** \brief Create an OrtSession to augment an existing model.
*
* Create an OrtSession with an existing model that will be augmented with additional nodes.
* Create an OrtSession with an existing model that will be augmented with additional nodes and initializers.
* Nodes can be added before or after the existing nodes in the model. ONNX Runtime will connect the nodes when the
* model is finalized.
*
* To add nodes to the existing model, first create an OrtModel using CreateModel.
* Add additional nodes and initializers to the OrtModel using AddNodeToGraph and AddInitializerToGraph.
* Graph inputs/outputs should be updated with SetGraphInputs and SetGraphOutputs to reflect changes made by the new
* nodes. The list of inputs/outputs should be for the overall model and not just the new nodes.
* To add nodes and initializers to the existing model, first create an OrtModel using CreateModel.
* Add nodes and initializers to the OrtModel using AddNodeToGraph and AddInitializerToGraph.
* Graph inputs/outputs should be updated with SetGraphInputs and SetGraphOutputs as needed to reflect changes made
* by the new nodes. The list of graph inputs/outputs should be for the overall model and not just the new nodes.
*
* Add the new information from the OrtModel to the original model using ApplyModelToSession, and prepare the
* session for inferencing by calling FinalizeModelBuilderSession.
@ -5304,14 +5304,14 @@ struct OrtModelBuilderApi {
/** \brief Create an OrtSession to augment an existing model.
*
* Create an OrtSession with an existing model that will be augmented with additional nodes.
* Create an OrtSession with an existing model that will be augmented with additional nodes and initializers.
* Nodes can be added before or after the existing nodes in the model. ONNX Runtime will connect the nodes when the
* model is finalized.
*
* To add nodes to the existing model, first create an OrtModel using CreateModel.
* Add additional nodes and initializers to the OrtModel using AddNodeToGraph and AddInitializerToGraph.
* Graph inputs/outputs should be updated with SetGraphInputs and SetGraphOutputs to reflect changes made by the new
* nodes. The list of inputs/outputs should be for the overall model and not just the new nodes.
* To add nodes and initializers to the existing model, first create an OrtModel using CreateModel.
* Add nodes and initializers to the OrtModel using AddNodeToGraph and AddInitializerToGraph.
* Graph inputs/outputs should be updated with SetGraphInputs and SetGraphOutputs as needed to reflect changes made
* by the new nodes. The list of graph inputs/outputs should be for the overall model and not just the new nodes.
*
* Add the new information from the OrtModel to the original model using ApplyModelToSession, and prepare the
* session for inferencing by calling FinalizeModelBuilderSession.

4
include/onnxruntime/core/session/onnxruntime_cxx_api.h
View file

@ -1122,8 +1122,8 @@ struct ConstSessionImpl : Base<T> {
int GetOpset(const std::string& domain) const; ///< Wraps OrtApi::SessionGetOpsetForDomain
// TODO: Should we move ValueInfo from ModelBuilderAPI to the ORT API? It will also be relevant to the Plugin EP API
// although the internal implementation may differ there.
// NOTE: We will probably move ValueInfo from ModelBuilderAPI to the ORT API as it will also be relevant to the Plugin EP API.
// Will move before checkin if that's the case.
std::vector<ModelBuilderAPI::ValueInfo> GetInputs() const;
std::vector<ModelBuilderAPI::ValueInfo> GetOutputs() const;
};

10
include/onnxruntime/core/session/onnxruntime_session_options_config_keys.h
View file

@ -300,3 +300,13 @@ static const char* const kOrtSessionOptionsQDQMatMulNBitsAccuracyLevel = "sessio
// “Default”: OS determines the scheduling priority and processor performance to service this workload. [Default]
// “Efficient”: OS treats this workload is efficiency oriented with low scheduling priority and efficient processor performance.
static const char* const kOrtEpDynamicOptionsWorkloadType = "ep.dynamic.workload_type";
// Create an Inference Session that will use the Model Builder API to create/update the model.
// This flag will create the session but not fully initialize it. A model, if provided, will be loaded.
// A session logger will be created, and execution providers will be registered.
// Any device specific allocators and IDataTransfer objects will be registered.
// This allows CreateAllocator to return device specific allocators registered by EPs.
// FUTURE: This will also allow CopyTensors to utilize the IDataTransfer objects
// "0": Disabled. [DEFAULT]
// "1": Enable Model Builder Session
static const char* const kOrtSessionOptionsEnableModelBuilder = "session.model_builder_session";

66
onnxruntime/test/shared_lib/test_model_builder_api.cc
View file

@ -369,8 +369,6 @@ TEST(ModelBuilderAPITest, Basic_CxxApi) {
ModelBuilderAPI::Model model(opsets);
model.AddGraph(graph);
auto session = CreateSession(*ort_env, model);
std::vector<Input<float>> inputs(1);
auto& input = inputs[0];
input.name = "X";
@ -381,6 +379,7 @@ TEST(ModelBuilderAPITest, Basic_CxxApi) {
std::vector<int64_t> expected_dims = {3, 8};
auto session = CreateSession(*ort_env, model);
TestInference<float>(session, inputs, "Z", expected_dims,
{340.0f, 360.0f, 380.0f, 400.0f, 420.0f, 440.0f, 460.0f, 480.0f,
596.0f, 648.0f, 700.0f, 752.0f, 804.0f, 856.0f, 908.0f, 960.0f,
@ -416,8 +415,8 @@ TEST(ModelBuilderAPITest, BasicModelEdit_CxxApi) {
ASSERT_EQ(graph_inputs[0].TypeInfo().GetTensorTypeAndShapeInfo().GetElementType(),
ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT);
// typically this isn't needed, but we want to replace this input and read info from it later on in the test
// validation so we move it out of the vector so it's saved locally.
// typically this isn't needed. we replace this input but need to read info from it later on in the test
// validation so we save the info locally to keep it accessible.
auto orig_input_name = graph_inputs[0].Name();
auto input_shape = graph_inputs[0].TypeInfo().GetTensorTypeAndShapeInfo().GetShape();
const std::string new_input_name = "Int64Input";
@ -622,11 +621,70 @@ TEST(ModelBuilderAPITest, InvalidModelEdit) {
}
TEST(ModelBuilderAPITest, CreateTypeInfo) {
const auto& api = Ort::GetApi();
TensorTypeAndShapeInfo base_tensor_info(ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT,
{2, 4});
OrtTypeInfo* base_tensor_type_info = nullptr;
Ort::ThrowOnError(api.CreateTensorTypeInfo(base_tensor_info, &base_tensor_type_info));
ONNXType onnx_type = ONNX_TYPE_UNKNOWN;
OrtTypeInfo* sparse_tensor_type_info = nullptr;
const OrtTensorTypeAndShapeInfo* tensor_info = nullptr;
ONNXTensorElementDataType onnx_element_type = ONNX_TENSOR_ELEMENT_DATA_TYPE_UNDEFINED;
// sparse tensor
Ort::ThrowOnError(api.CreateSparseTensorTypeInfo(base_tensor_info, &sparse_tensor_type_info));
Ort::ThrowOnError(api.GetOnnxTypeFromTypeInfo(sparse_tensor_type_info, &onnx_type));
ASSERT_EQ(onnx_type, ONNXType::ONNX_TYPE_SPARSETENSOR);
Ort::ThrowOnError(api.CastTypeInfoToTensorInfo(sparse_tensor_type_info, &tensor_info));
Ort::ThrowOnError(api.GetTensorElementType(tensor_info, &onnx_element_type));
ASSERT_EQ(onnx_element_type, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT);
// sequence
OrtTypeInfo* sequence_type_info = nullptr;
const OrtSequenceTypeInfo* sequence_info = nullptr;
OrtTypeInfo* sequence_element_type_info = nullptr;
Ort::ThrowOnError(api.CreateSequenceTypeInfo(base_tensor_type_info, &sequence_type_info));
Ort::ThrowOnError(api.GetOnnxTypeFromTypeInfo(sequence_type_info, &onnx_type));
ASSERT_EQ(onnx_type, ONNXType::ONNX_TYPE_SEQUENCE);
Ort::ThrowOnError(api.CastTypeInfoToSequenceTypeInfo(sequence_type_info, &sequence_info));
Ort::ThrowOnError(api.GetSequenceElementType(sequence_info, &sequence_element_type_info));
Ort::ThrowOnError(api.GetOnnxTypeFromTypeInfo(sequence_element_type_info, &onnx_type));
ASSERT_EQ(onnx_type, ONNXType::ONNX_TYPE_TENSOR);
Ort::ThrowOnError(api.CastTypeInfoToTensorInfo(sequence_element_type_info, &tensor_info));
Ort::ThrowOnError(api.GetTensorElementType(tensor_info, &onnx_element_type));
ASSERT_EQ(onnx_element_type, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT);
// map
OrtTypeInfo* map_type_info = nullptr;
const OrtMapTypeInfo* map_info = nullptr;
ONNXTensorElementDataType map_key_type = ONNX_TENSOR_ELEMENT_DATA_TYPE_UNDEFINED;
OrtTypeInfo* map_value_type_info = nullptr;
Ort::ThrowOnError(api.CreateMapTypeInfo(ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64, base_tensor_type_info,
&map_type_info));
Ort::ThrowOnError(api.GetOnnxTypeFromTypeInfo(map_type_info, &onnx_type));
ASSERT_EQ(onnx_type, ONNXType::ONNX_TYPE_MAP);
Ort::ThrowOnError(api.CastTypeInfoToMapTypeInfo(map_type_info, &map_info));
Ort::ThrowOnError(api.GetMapKeyType(map_info, &map_key_type));
ASSERT_EQ(map_key_type, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64);
Ort::ThrowOnError(api.GetMapValueType(map_info, &map_value_type_info));
Ort::ThrowOnError(api.GetOnnxTypeFromTypeInfo(map_value_type_info, &onnx_type));
ASSERT_EQ(onnx_type, ONNXType::ONNX_TYPE_TENSOR);
Ort::ThrowOnError(api.CastTypeInfoToTensorInfo(map_value_type_info, &tensor_info));
Ort::ThrowOnError(api.GetTensorElementType(tensor_info, &onnx_element_type));
ASSERT_EQ(onnx_element_type, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT);
// optional
OrtTypeInfo* optional_type_info = nullptr;
const OrtOptionalTypeInfo* optional_info = nullptr;
OrtTypeInfo* optional_contained_type_info = nullptr;
Ort::ThrowOnError(api.CreateOptionalTypeInfo(base_tensor_type_info, &optional_type_info));
Ort::ThrowOnError(api.GetOnnxTypeFromTypeInfo(optional_type_info, &onnx_type));
ASSERT_EQ(onnx_type, ONNXType::ONNX_TYPE_OPTIONAL);
Ort::ThrowOnError(api.CastTypeInfoToOptionalTypeInfo(optional_type_info, &optional_info));
Ort::ThrowOnError(api.GetOptionalContainedTypeInfo(optional_info, &optional_contained_type_info));
Ort::ThrowOnError(api.GetOnnxTypeFromTypeInfo(optional_contained_type_info, &onnx_type));
ASSERT_EQ(onnx_type, ONNXType::ONNX_TYPE_TENSOR);
}