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Merged
zhangxiang1993 merged 10 commits into from
Jan 24, 2020
Merged

User/xianz/winml adapter c api #2869

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d8b4fac
wrapper all existing winml adapter apis with API_IMPL to try catch
zhangxiang1993 Jan 16, 2020
ef9a412
Merge branch 'user/sheilk/winml-adapter-c-api' of https://github.com/…
zhangxiang1993 Jan 17, 2020
044c861
Return HR or Throw for WinML adapter APIs if failed
zhangxiang1993 Jan 18, 2020
e519d73
merge branch user/sheil/winmladapter_c_api
zhangxiang1993 Jan 18, 2020
ddac824
resolve conflicts
zhangxiang1993 Jan 21, 2020
0705e68
undo macro wrapper for two places
zhangxiang1993 Jan 21, 2020
c39e8aa
merge windowsai
zhangxiang1993 Jan 22, 2020
fa65018
Wrap error macros around ort apis, too.
zhangxiang1993 Jan 22, 2020
d0b89aa
merge origin branch
zhangxiang1993 Jan 22, 2020
f06434b
resolve conflicts
zhangxiang1993 Jan 24, 2020
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2 changes: 2 additions & 0 deletions winml/adapter/winml_adapter_model.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -310,9 +310,11 @@ ORT_API_STATUS_IMPL(winmla::ModelGetOutputName, const OrtModel* model, size_t in
}

ORT_API_STATUS_IMPL(winmla::ModelGetInputDescription, const OrtModel* model, size_t index, const char** input_description, size_t* count) {
API_IMPL_BEGIN
*input_description = model->UseModelInfo()->input_features_[index]->doc_string().c_str();
*count = model->UseModelInfo()->input_features_[index]->doc_string().size();
return nullptr;
API_IMPL_END
}

ORT_API_STATUS_IMPL(winmla::ModelGetOutputDescription, const OrtModel* model, size_t index, const char** output_description, size_t* count) {
Expand Down
27 changes: 16 additions & 11 deletions winml/lib/Api.Ort/OnnxruntimeCpuSessionBuilder.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@

#include "OnnxruntimeCpuSessionBuilder.h"
#include "OnnxruntimeEngine.h"
#include "OnnxruntimeErrors.h"

using namespace Windows::AI::MachineLearning;

Expand All @@ -22,33 +23,36 @@ OnnxruntimeCpuSessionBuilder::CreateSessionOptions(
auto winml_adapter_api = engine_factory_->UseWinmlAdapterApi();

OrtSessionOptions* ort_options;
ort_api->CreateSessionOptions(&ort_options);
RETURN_HR_IF_NOT_OK_MSG(ort_api->CreateSessionOptions(&ort_options),
ort_api);

auto session_options = UniqueOrtSessionOptions(ort_options, ort_api->ReleaseSessionOptions);

// set the graph optimization level to all (used to be called level 3)
ort_api->SetSessionGraphOptimizationLevel(session_options.get(), GraphOptimizationLevel::ORT_ENABLE_ALL);
RETURN_HR_IF_NOT_OK_MSG(ort_api->SetSessionGraphOptimizationLevel(session_options.get(), GraphOptimizationLevel::ORT_ENABLE_ALL),
ort_api);

// Onnxruntime will use half the number of concurrent threads supported on the system
// by default. This causes MLAS to not exercise every logical core.
// We force the thread pool size to be maxxed out to ensure that WinML always
// runs the fastest.
ort_api->SetIntraOpNumThreads(session_options.get(), std::thread::hardware_concurrency());
RETURN_HR_IF_NOT_OK_MSG(ort_api->SetIntraOpNumThreads(session_options.get(), std::thread::hardware_concurrency()),
ort_api);

#ifndef _WIN64
auto use_arena = false;
#else
auto use_arena = true;
#endif
winml_adapter_api->OrtSessionOptionsAppendExecutionProvider_CPU(session_options.get(), use_arena);
RETURN_HR_IF_NOT_OK_MSG(winml_adapter_api->OrtSessionOptionsAppendExecutionProvider_CPU(session_options.get(), use_arena),
ort_api);

// call release() so the underlying OrtSessionOptions object isn't freed
*options = session_options.release();

return S_OK;
}


HRESULT
OnnxruntimeCpuSessionBuilder::CreateSession(
OrtSessionOptions* options,
Expand All @@ -62,11 +66,13 @@ OnnxruntimeCpuSessionBuilder::CreateSession(
RETURN_IF_FAILED(engine_factory_->GetOrtEnvironment(&ort_env));

OrtSession* ort_session_raw;
winml_adapter_api->CreateSessionWithoutModel(ort_env, options, &ort_session_raw);
RETURN_HR_IF_NOT_OK_MSG(winml_adapter_api->CreateSessionWithoutModel(ort_env, options, &ort_session_raw),
engine_factory_->UseOrtApi());

auto ort_session = UniqueOrtSession(ort_session_raw, ort_api->ReleaseSession);

*session = ort_session.release();

return S_OK;
}

Expand All @@ -76,9 +82,8 @@ OnnxruntimeCpuSessionBuilder::Initialize(
RETURN_HR_IF_NULL(E_INVALIDARG, session);

auto winml_adapter_api = engine_factory_->UseWinmlAdapterApi();
if (auto status = winml_adapter_api->SessionInitialize(session)) {
return E_FAIL;
}
RETURN_HR_IF_NOT_OK_MSG(winml_adapter_api->SessionInitialize(session),
engine_factory_->UseOrtApi());

return S_OK;
}
122 changes: 57 additions & 65 deletions winml/lib/Api.Ort/OnnxruntimeDescriptorConverter.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,8 @@

#include "OnnxruntimeEngine.h"

#include "OnnxruntimeErrors.h"

using namespace winrt::Windows::AI::MachineLearning;

// BitmapPixelFormat constants
Expand Down Expand Up @@ -44,12 +46,11 @@ static const char* c_supported_nominal_ranges[] =

namespace Windows::AI::MachineLearning {


// Forward declare CreateFeatureDescriptor
static winml::ILearningModelFeatureDescriptor
CreateFeatureDescriptor(
OnnxruntimeEngineFactory* engine_factory,
const OnnxruntimeValueInfoWrapper* feature_descriptor,
const OnnxruntimeValueInfoWrapper* feature_descriptor,
const std::unordered_map<std::string, std::string>& metadata);

static TensorKind
Expand Down Expand Up @@ -100,7 +101,9 @@ TensorKindFromONNXTensorElementDataType(ONNXTensorElementDataType dataType) {
case ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_COMPLEX128: {
return TensorKind::Complex128;
}
default: { return TensorKind::Undefined; }
default: {
return TensorKind::Undefined;
}
}
}

Expand Down Expand Up @@ -153,7 +156,9 @@ TensorKindToString(TensorKind tensorKind) {
return "complex128";
}
case TensorKind::Undefined:
default: { return "undefined"; }
default: {

@zhangxiang1993 zhangxiang1993 Jan 18, 2020 •
edited
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I did clang format for these files. #Resolved

return "undefined";
}
}
}

Expand Down Expand Up @@ -310,9 +315,8 @@ GetTensorType(
const std::unordered_map<std::string, std::string>& metadata) {
const char* denotation;
size_t len;
if (auto status = engine_factory->UseWinmlAdapterApi()->GetDenotationFromTypeInfo(type_info, &denotation, &len)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseWinmlAdapterApi()->GetDenotationFromTypeInfo(type_info, &denotation, &len),
engine_factory->UseOrtApi());

constexpr char c_image[] = "IMAGE";
auto has_image_denotation = strncmp(denotation, c_image, _countof(c_image)) == 0;
Expand All @@ -327,14 +331,12 @@ GetTensorType(
// Check if the tensor value_info_proto is of type float.
// IMAGE tensors MUST be of type float
const OrtTensorTypeAndShapeInfo* tensor_info;
if (auto status = engine_factory->UseOrtApi()->CastTypeInfoToTensorInfo(type_info, &tensor_info)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->CastTypeInfoToTensorInfo(type_info, &tensor_info),
engine_factory->UseOrtApi());

ONNXTensorElementDataType tensor_element_data_type;
if (auto status = engine_factory->UseOrtApi()->GetTensorElementType(tensor_info, &tensor_element_data_type)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->GetTensorElementType(tensor_info, &tensor_element_data_type),
engine_factory->UseOrtApi());

auto tensor_kind = WinML::TensorKindFromONNXTensorElementDataType(tensor_element_data_type);
auto is_float_tensor = tensor_kind == TensorKind::Float;
Expand Down Expand Up @@ -396,36 +398,32 @@ GetTensorType(
static winml::ILearningModelFeatureDescriptor
CreateTensorFeatureDescriptor(
OnnxruntimeEngineFactory* engine_factory,
const OnnxruntimeValueInfoWrapper* feature_descriptor,
const OnnxruntimeValueInfoWrapper* feature_descriptor,
const std::unordered_map<std::string, std::string>& metadata,
bool has_unsupported_image_metadata) {
auto type_info = feature_descriptor->type_info_.get();

const OrtTensorTypeAndShapeInfo* tensor_info;
if (auto status = engine_factory->UseOrtApi()->CastTypeInfoToTensorInfo(type_info, &tensor_info)) {
throw; //TODO fix throw here!;
}

THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->CastTypeInfoToTensorInfo(type_info, &tensor_info),
engine_factory->UseOrtApi());
size_t num_dims;
if (auto status = engine_factory->UseOrtApi()->GetDimensionsCount(tensor_info, &num_dims)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->GetDimensionsCount(tensor_info, &num_dims),
engine_factory->UseOrtApi());

auto shape = std::vector<int64_t>(num_dims);
if (auto status = engine_factory->UseOrtApi()->GetDimensions(tensor_info, shape.data(), shape.size())) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->GetDimensions(tensor_info, shape.data(), shape.size()),
engine_factory->UseOrtApi());

ONNXTensorElementDataType tensor_element_data_type;
if (auto status = engine_factory->UseOrtApi()->GetTensorElementType(tensor_info, &tensor_element_data_type)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->GetTensorElementType(tensor_info, &tensor_element_data_type),
engine_factory->UseOrtApi());

auto kind = WinML::TensorKindFromONNXTensorElementDataType(tensor_element_data_type);

auto descriptor = winrt::make<winmlp::TensorFeatureDescriptor>(
WinML::Strings::HStringFromUTF8(feature_descriptor->name_),
WinML::Strings::HStringFromUTF8(feature_descriptor->description_), // description
feature_descriptor->name_length_ > 0, // is_required
feature_descriptor->name_length_ > 0, // is_required
kind,
shape,
has_unsupported_image_metadata);
Expand All @@ -436,32 +434,27 @@ CreateTensorFeatureDescriptor(
static winml::ILearningModelFeatureDescriptor
CreateImageFeatureDescriptor(
OnnxruntimeEngineFactory* engine_factory,
const OnnxruntimeValueInfoWrapper* feature_descriptor,
const OnnxruntimeValueInfoWrapper* feature_descriptor,
const std::unordered_map<std::string, std::string>& metadata) {
auto type_info = feature_descriptor->type_info_.get();

const OrtTensorTypeAndShapeInfo* tensor_info;
if (auto status = engine_factory->UseOrtApi()->CastTypeInfoToTensorInfo(type_info, &tensor_info)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->CastTypeInfoToTensorInfo(type_info, &tensor_info),
engine_factory->UseOrtApi());

size_t num_dims;
if (auto status = engine_factory->UseOrtApi()->GetDimensionsCount(tensor_info, &num_dims)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->GetDimensionsCount(tensor_info, &num_dims),
engine_factory->UseOrtApi());

auto shape = std::vector<int64_t>(num_dims);
if (auto status = engine_factory->UseOrtApi()->GetDimensions(tensor_info, shape.data(), shape.size())) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->GetDimensions(tensor_info, shape.data(), shape.size()),
engine_factory->UseOrtApi());

ONNXTensorElementDataType tensor_element_data_type;
if (auto status = engine_factory->UseOrtApi()->GetTensorElementType(tensor_info, &tensor_element_data_type)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->GetTensorElementType(tensor_info, &tensor_element_data_type),
engine_factory->UseOrtApi());
auto kind = WinML::TensorKindFromONNXTensorElementDataType(tensor_element_data_type);


// pixel format and alpha
auto pixel_format_value = FetchMetadataValueOrNull(metadata, c_bitmap_pixel_format_key);
auto format_info = CreateBitmapPixelFormatAndAlphaModeInfo(pixel_format_value);
Expand All @@ -473,12 +466,12 @@ CreateImageFeatureDescriptor(
// to TensorFeatureDescriptor (invalid image metadata)
#ifdef DONE_LAYERING
// color space gamma value
auto color_space_gamma_value = FetchMetadataValueOrNull(metadata, c_color_space_key);
auto color_space_gamma = CreateImageColorSpaceGamma(color_space_gamma_value);
auto color_space_gamma_value = FetchMetadataValueOrNull(metadata, c_color_space_key);
auto color_space_gamma = CreateImageColorSpaceGamma(color_space_gamma_value);

// nominal range
auto nominal_range_value = FetchMetadataValueOrNull(metadata, c_nominal_range_key);
auto nominal_range = CreateImageNominalPixelRange(nominal_range_value);
auto nominal_range_value = FetchMetadataValueOrNull(metadata, c_nominal_range_key);
auto nominal_range = CreateImageNominalPixelRange(nominal_range_value);
#endif

// The current code assumes that the shape will be in NCHW.
Expand All @@ -504,25 +497,24 @@ CreateImageFeatureDescriptor(
static winml::ILearningModelFeatureDescriptor
CreateMapFeatureDescriptor(
OnnxruntimeEngineFactory* engine_factory,
const OnnxruntimeValueInfoWrapper* feature_descriptor,
const OnnxruntimeValueInfoWrapper* feature_descriptor,
const std::unordered_map<std::string, std::string>& metadata) {
auto type_info = feature_descriptor->type_info_.get();

const OrtMapTypeInfo* map_info;
if (auto status = engine_factory->UseWinmlAdapterApi()->CastTypeInfoToMapTypeInfo(type_info, &map_info)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseWinmlAdapterApi()->CastTypeInfoToMapTypeInfo(type_info, &map_info),
engine_factory->UseOrtApi());

ONNXTensorElementDataType map_key_data_type;
if (auto status = engine_factory->UseWinmlAdapterApi()->GetMapKeyType(map_info, &map_key_data_type)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseWinmlAdapterApi()->GetMapKeyType(map_info, &map_key_data_type),
engine_factory->UseOrtApi());

auto key_kind = WinML::TensorKindFromONNXTensorElementDataType(map_key_data_type);

OrtTypeInfo* map_value_type_info;
if (auto status = engine_factory->UseWinmlAdapterApi()->GetMapValueType(map_info, &map_value_type_info)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseWinmlAdapterApi()->GetMapValueType(map_info, &map_value_type_info),
engine_factory->UseOrtApi());

UniqueOrtTypeInfo unique_map_value_type_info(map_value_type_info, engine_factory->UseOrtApi()->ReleaseTypeInfo);

OnnxruntimeValueInfoWrapper dummy_ort_value_info_wrapper;
Expand All @@ -535,7 +527,6 @@ CreateMapFeatureDescriptor(
auto value_descriptor =
CreateFeatureDescriptor(engine_factory, &dummy_ort_value_info_wrapper, metadata);


auto descriptor = winrt::make<winmlp::MapFeatureDescriptor>(
WinML::Strings::HStringFromUTF8(feature_descriptor->name_),
WinML::Strings::HStringFromUTF8(feature_descriptor->description_),
Expand All @@ -548,19 +539,18 @@ CreateMapFeatureDescriptor(
static winml::ILearningModelFeatureDescriptor
CreateSequenceFeatureDescriptor(
OnnxruntimeEngineFactory* engine_factory,
const OnnxruntimeValueInfoWrapper* feature_descriptor,
const OnnxruntimeValueInfoWrapper* feature_descriptor,
const std::unordered_map<std::string, std::string>& metadata) {
auto type_info = feature_descriptor->type_info_.get();

const OrtSequenceTypeInfo* sequence_info;
if (auto status = engine_factory->UseWinmlAdapterApi()->CastTypeInfoToSequenceTypeInfo(type_info, &sequence_info)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseWinmlAdapterApi()->CastTypeInfoToSequenceTypeInfo(type_info, &sequence_info),
engine_factory->UseOrtApi());

OrtTypeInfo* sequence_element_type_info;
if (auto status = engine_factory->UseWinmlAdapterApi()->GetSequenceElementType(sequence_info, &sequence_element_type_info)) {
throw; //TODO fix throw here!;
}
THROW_IF_NOT_OK_MSG(engine_factory->UseWinmlAdapterApi()->GetSequenceElementType(sequence_info, &sequence_element_type_info),
engine_factory->UseOrtApi());

UniqueOrtTypeInfo unique_sequence_element_type_info(sequence_element_type_info, engine_factory->UseOrtApi()->ReleaseTypeInfo);

OnnxruntimeValueInfoWrapper dummy_ort_value_info_wrapper;
Expand Down Expand Up @@ -590,7 +580,9 @@ CreateFeatureDescriptor(
auto type_info = feature_descriptor->type_info_.get();

ONNXType onnx_type;
engine_factory->UseOrtApi()->GetOnnxTypeFromTypeInfo(type_info, &onnx_type);
THROW_IF_NOT_OK_MSG(engine_factory->UseOrtApi()->GetOnnxTypeFromTypeInfo(type_info, &onnx_type),
engine_factory->UseOrtApi());

switch (onnx_type) {
case ONNXType::ONNX_TYPE_TENSOR: {
auto tensor_type = GetTensorType(engine_factory, type_info, metadata);
Expand Down
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