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Enable model subgraph execution in OVEP and setting the OpenVINO dll's to the path from the OpenVINO pypi packge in OVEP and fix OVEP windows io buffer sample (#16147)

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### Description
This PR enables execution of subgraphs in OVEP and currently, when OVEP
developers install the onnxruntime-openvino package on windows from
pypi, they would have to additionally download OpenVINO windows binaries
and run the setupvars.bat script which sets the environment PATH to
locate the OV dll's. Also this PR fixes issues of OVEP windows io buffer
sample.



### Motivation and Context
Fix: We want to make the user experience easy for OVEP Python developers
on windows platform.
This fix, introduces a function add_openvino_libs_to_path at the
location tools/python/util/add_openvino_win_libs.py.
The above function, can be called by OVEP python users in the
application code and that takes care of setting
the OpenVINO dll's to the path from the OpenVINO pypi packge (openvino)
which was installed.
This change also makes sure that add_openvino_libs_to_path() function is
added to onnxruntime python package
only when it is build for OpenVINO Execution Provider for ONNXRuntime
and not for default ORT python package builds.

New user experience for Python OVEP developers on windows platform:
step 1: pip install onnxruntime-openvino
step 2: pip install openvino
step 3: <Add these 2 lines in the application code>
import onnxruntime.tools.add_openvino_win_libs as utils
utils.add_openvino_libs_to_path()

---------

Signed-off-by: MaajidKhan <n.maajid.khan@intel.com>
Co-authored-by: MaajidKhan <n.maajid.khan@intel.com>
Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.com>
This commit is contained in:
saurabh 2023-06-16 19:47:09 -07:00 committed by GitHub
parent 3f7f90aed0
commit a6ce7b339f
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
12 changed files with 400 additions and 354 deletions
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2
cmake/onnxruntime_providers.cmake
View file

@ -880,7 +880,7 @@ if (onnxruntime_USE_OPENVINO)
target_compile_options(onnxruntime_providers_openvino PRIVATE "-Wno-parentheses")
endif()
add_dependencies(onnxruntime_providers_openvino onnxruntime_providers_shared ${onnxruntime_EXTERNAL_DEPENDENCIES})
target_include_directories(onnxruntime_providers_openvino SYSTEM PUBLIC ${ONNXRUNTIME_ROOT} ${CMAKE_CURRENT_BINARY_DIR} ${eigen_INCLUDE_DIRS} ${OpenVINO_INCLUDE_DIR} ${OPENVINO_INCLUDE_DIR_LIST} ${PYTHON_INCLUDE_DIRS} $ENV{OPENCL_INCS})
target_include_directories(onnxruntime_providers_openvino SYSTEM PUBLIC ${ONNXRUNTIME_ROOT} ${CMAKE_CURRENT_BINARY_DIR} ${eigen_INCLUDE_DIRS} ${OpenVINO_INCLUDE_DIR} ${OPENVINO_INCLUDE_DIR_LIST} ${PYTHON_INCLUDE_DIRS} $ENV{OPENCL_INCS} $ENV{OPENCL_INCS}/../../cl_headers/)
target_link_libraries(onnxruntime_providers_openvino ${ONNXRUNTIME_PROVIDERS_SHARED} Boost::mp11 ${OPENVINO_LIB_LIST} ${ABSEIL_LIBS})
target_compile_definitions(onnxruntime_providers_openvino PRIVATE VER_MAJOR=${VERSION_MAJOR_PART})

15
cmake/onnxruntime_python.cmake
View file

@ -504,6 +504,12 @@ file(GLOB onnxruntime_qdq_helper_srcs CONFIGURE_DEPENDS
${REPO_ROOT}/tools/python/util/qdq_helpers/*.py
)
if (onnxruntime_USE_OPENVINO)
file(GLOB onnxruntime_python_openvino_python_srcs CONFIGURE_DEPENDS
${REPO_ROOT}/tools/python/util/add_openvino_win_libs.py
)
endif()
set(build_output_target onnxruntime_common)
if(NOT onnxruntime_ENABLE_STATIC_ANALYSIS)
add_custom_command(
@ -630,6 +636,15 @@ add_custom_command(
$<TARGET_FILE_DIR:${build_output_target}>
)
if (onnxruntime_USE_OPENVINO)
add_custom_command(
TARGET onnxruntime_pybind11_state POST_BUILD
COMMAND ${CMAKE_COMMAND} -E copy
${onnxruntime_python_openvino_python_srcs}
$<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/tools/
)
endif()
if (onnxruntime_ENABLE_EXTERNAL_CUSTOM_OP_SCHEMAS)
add_custom_command(
TARGET onnxruntime_pybind11_state POST_BUILD

4
dockerfiles/Dockerfile.openvino
View file

@ -1,9 +1,9 @@
#-------------------------------------------------------------------------
# Copyright(C) 2021 Intel Corporation.
# Copyright(C) 2021-2023 Intel Corporation.
# SPDX-License-Identifier: MIT
#--------------------------------------------------------------------------
ARG OPENVINO_VERSION=2022.3.0
ARG OPENVINO_VERSION=2023.0.0
# Build stage

4
dockerfiles/Dockerfile.openvino-csharp
View file

@ -1,9 +1,9 @@
#-------------------------------------------------------------------------
# Copyright(C) 2021 Intel Corporation.
# Copyright(C) 2021-2023 Intel Corporation.
# SPDX-License-Identifier: MIT
#--------------------------------------------------------------------------
ARG OPENVINO_VERSION=2022.3.0
ARG OPENVINO_VERSION=2023.0.0
# Build stage
FROM openvino/ubuntu20_runtime:${OPENVINO_VERSION} AS base

6
docs/python/ReadMeOV.rst
View file

@ -15,18 +15,18 @@ Requirements
^^^^^^^^^^^^
- Ubuntu 18.04, 20.04, RHEL(CPU only) or Windows 10 - 64 bit
- Python 3.7, 3.8 or 3.9 for Linux and only Python3.9 for Windows
- Python 3.8, 3.9 or 3.10 for Linux and only Python3.10 for Windows
This package supports:
- Intel® CPUs
- Intel® integrated GPUs
``pip3 install onnxruntime-openvino==1.13.1``
``pip3 install onnxruntime-openvino``
Please install OpenVINO™ PyPi Package separately for Windows.
For installation instructions on Windows please refer to `OpenVINO™ Execution Provider for ONNX Runtime for Windows <https://github.com/intel/onnxruntime/releases/>`_.
**OpenVINO™ Execution Provider for ONNX Runtime** Linux Wheels comes with pre-built libraries of OpenVINO™ version 2022.2.0 eliminating the need to install OpenVINO™ separately. The OpenVINO™ libraries are prebuilt with CXX11_ABI flag set to 0.
**OpenVINO™ Execution Provider for ONNX Runtime** Linux Wheels comes with pre-built libraries of OpenVINO™ version 2023.0.0 eliminating the need to install OpenVINO™ separately. The OpenVINO™ libraries are prebuilt with CXX11_ABI flag set to 0.
The package also includes module that is used by torch-ort-inference to accelerate inference for PyTorch models with OpenVINO Execution Provider.
See `torch-ort-inference <https://github.com/pytorch/ort#accelerate-inference-for-pytorch-models-with-onnx-runtime-preview>`_ for more details.

659
onnxruntime/core/providers/openvino/backends/basic_backend.cc
View file

@ -56,21 +56,10 @@ BasicBackend::BasicBackend(const ONNX_NAMESPACE::ModelProto& model_proto,
exe_network_ = global_context_.ie_core.LoadNetwork(ie_cnn_network_, remote_context_, subgraph_context_.subgraph_name);
LOGS_DEFAULT(INFO) << log_tag << "Loaded model to the plugin";
} else {
#if defined(OPENVINO_2023_0)
if (subgraph_context.precision != InferenceEngine::Precision::FP16) {
const std::string model = model_proto.SerializeAsString();
exe_network_ = global_context_.ie_core.LoadNetwork(model, hw_target, device_config, subgraph_context_.subgraph_name);
LOGS_DEFAULT(INFO) << log_tag << "Loaded model to the plugin";
} else {
ie_cnn_network_ = CreateOVModel(model_proto, global_context_, subgraph_context_, const_outputs_map_);
exe_network_ = global_context_.ie_core.LoadNetwork(ie_cnn_network_, hw_target, device_config, subgraph_context_.subgraph_name);
LOGS_DEFAULT(INFO) << log_tag << "Loaded model to the plugin";
}
#else
ie_cnn_network_ = CreateOVModel(model_proto, global_context_, subgraph_context_, const_outputs_map_);
exe_network_ = global_context_.ie_core.LoadNetwork(ie_cnn_network_, hw_target, device_config, subgraph_context_.subgraph_name);
LOGS_DEFAULT(INFO) << log_tag << "Loaded model to the plugin";
#endif
}
#else
#if defined(OPENVINO_2023_0)
if (subgraph_context.precision != InferenceEngine::Precision::FP16 && global_context_.enable_dynamic_shapes == false) {
@ -88,357 +77,355 @@ BasicBackend::BasicBackend(const ONNX_NAMESPACE::ModelProto& model_proto,
LOGS_DEFAULT(INFO) << log_tag << "Loaded model to the plugin";
#endif
#endif
}
else {
ie_cnn_network_ = CreateOVModel(model_proto, global_context_, subgraph_context_, const_outputs_map_);
exe_network_ = global_context_.ie_core.LoadNetwork(ie_cnn_network_, hw_target, device_config, subgraph_context_.subgraph_name);
LOGS_DEFAULT(INFO) << log_tag << "Loaded model to the plugin";
}
}
catch (const char* msg) {
throw(msg);
} else {
ie_cnn_network_ = CreateOVModel(model_proto, global_context_, subgraph_context_, const_outputs_map_);
exe_network_ = global_context_.ie_core.LoadNetwork(ie_cnn_network_, hw_target, device_config, subgraph_context_.subgraph_name);
LOGS_DEFAULT(INFO) << log_tag << "Loaded model to the plugin";
}
} catch (const char* msg) {
throw(msg);
}
// The infer_requests_ pool will be intialized with a default value of 8 infer_request's
// The nireq value can also be configured to any num_of_threads during runtime
size_t nireq = global_context_.num_of_threads;
LOGS_DEFAULT(INFO) << log_tag << "The value of nireq being used is: " << nireq;
// The infer_requests_ pool will be intialized with a default value of 8 infer_request's
// The nireq value can also be configured to any num_of_threads during runtime
size_t nireq = global_context_.num_of_threads;
LOGS_DEFAULT(INFO) << log_tag << "The value of nireq being used is: " << nireq;
#ifndef NDEBUG
if (openvino_ep::backend_utils::IsDebugEnabled()) {
std::cout << "The value of nireq being used is: " << nireq << std::endl;
}
if (openvino_ep::backend_utils::IsDebugEnabled()) {
std::cout << "The value of nireq being used is: " << nireq << std::endl;
}
#endif
inferRequestsQueue_ = std::unique_ptr<InferRequestsQueue>(new InferRequestsQueue(exe_network_, nireq));
}
inferRequestsQueue_ = std::unique_ptr<InferRequestsQueue>(new InferRequestsQueue(exe_network_, nireq));
}
bool BasicBackend::ValidateSubgraph(std::map<std::string, std::shared_ptr<ngraph::Node>> & const_outputs_map) {
if (const_outputs_map.size() == subgraph_context_.output_names.size())
subgraph_context_.is_constant = true;
if (subgraph_context_.is_constant) {
LOGS_DEFAULT(INFO) << log_tag << "The subgraph is a const. Directly moving to Infer stage.";
return true;
}
return false;
bool BasicBackend::ValidateSubgraph(std::map<std::string, std::shared_ptr<ngraph::Node>>& const_outputs_map) {
if (const_outputs_map.size() == subgraph_context_.output_names.size())
subgraph_context_.is_constant = true;
if (subgraph_context_.is_constant) {
LOGS_DEFAULT(INFO) << log_tag << "The subgraph is a const. Directly moving to Infer stage.";
return true;
}
return false;
}
void BasicBackend::PopulateConfigValue(ov::AnyMap & device_config) {
// Set inference precision if device_type != AUTO
// if (global_context_.device_type.find("GPU_FP16")!= std::string::npos){
// device_config.emplace(ov::hint::inference_precision(global_context_.precision_str));
// }
device_config = {};
void BasicBackend::PopulateConfigValue(ov::AnyMap& device_config) {
// Set inference precision if device_type != AUTO
// if (global_context_.device_type.find("GPU_FP16")!= std::string::npos){
// device_config.emplace(ov::hint::inference_precision(global_context_.precision_str));
// }
device_config = {};
#ifndef NDEBUG
if (openvino_ep::backend_utils::IsDebugEnabled()) {
device_config.emplace(ov::enable_profiling(true));
}
if (openvino_ep::backend_utils::IsDebugEnabled()) {
device_config.emplace(ov::enable_profiling(true));
}
#endif
#if defined(OPENVINO_2023_0)
if (global_context_.device_type.find("VPUX") != std::string::npos) {
std::pair<std::string, ov::Any> device_property;
device_property = std::make_pair("VPUX_COMPILER_TYPE", "MLIR");
device_config.emplace(ov::device::properties("VPUX", device_property));
}
#endif
if (global_context_.device_type.find("VPUX") != std::string::npos) {
std::pair<std::string, ov::Any> device_property;
device_property = std::make_pair("VPUX_COMPILER_TYPE", "MLIR");
device_config.emplace(ov::device::properties("VPUX", device_property));
}
#endif
}
void BasicBackend::EnableCaching() {
if (!global_context_.cache_dir.empty()) {
if (global_context_.is_wholly_supported_graph) {
void BasicBackend::EnableCaching() {
if (!global_context_.cache_dir.empty()) {
if (global_context_.is_wholly_supported_graph) {
#if defined(OPENVINO_2022_3)
#if defined(_WIN32) || defined(WIN32) || defined(__CYGWIN__) || defined(__MINGW32__) || defined(__BORLANDC__)
_putenv_s("OV_GPU_CACHE_MODEL", "1");
_putenv_s("OV_GPU_CACHE_MODEL", "1");
#else
setenv("OV_GPU_CACHE_MODEL", "1", 1);
setenv("OV_GPU_CACHE_MODEL", "1", 1);
#endif
#endif
}
LOGS_DEFAULT(INFO) << log_tag << "Enables Caching";
global_context_.ie_core.SetCache(global_context_.cache_dir);
}
LOGS_DEFAULT(INFO) << log_tag << "Enables Caching";
global_context_.ie_core.SetCache(global_context_.cache_dir);
}
}
void BasicBackend::EnableGPUThrottling(ov::AnyMap & device_config) {
if (global_context_.enable_opencl_throttling == true && global_context_.device_type.find("GPU") != std::string::npos) {
LOGS_DEFAULT(INFO) << log_tag << "Enabled OpenCL queue throttling for GPU device";
device_config[GPU_CONFIG_KEY(PLUGIN_THROTTLE)] = "1";
}
void BasicBackend::EnableGPUThrottling(ov::AnyMap& device_config) {
if (global_context_.enable_opencl_throttling == true && global_context_.device_type.find("GPU") != std::string::npos) {
LOGS_DEFAULT(INFO) << log_tag << "Enabled OpenCL queue throttling for GPU device";
device_config[GPU_CONFIG_KEY(PLUGIN_THROTTLE)] = "1";
}
}
// Starts an asynchronous inference request for data in slice indexed by batch_slice_idx on
// an Infer Request indexed by infer_req_idx
void BasicBackend::StartAsyncInference(Ort::KernelContext & context, OVInferRequestPtr infer_request) {
try {
auto graph_input_info = exe_network_.Get().inputs();
int input_idx = 0;
for (auto input_info_iter = graph_input_info.begin();
input_info_iter != graph_input_info.end(); ++input_info_iter) {
auto input_names = input_info_iter->get_names();
std::string onnx_input_name;
std::string input_name;
// use names retrieved from original ONNX model to assign the right onnx input name for the graph
for (auto it = subgraph_context_.input_names.begin(); it != subgraph_context_.input_names.end(); ++it) {
if (it->second == input_idx) {
onnx_input_name = it->first;
break;
}
}
// using the input name retrieved from ONNX original to match with the input names returned by OV tensors
if (input_names.find(onnx_input_name) != input_names.end()) {
input_name = onnx_input_name;
} else {
throw(log_tag + "Input names mismatch between OpenVINO and ONNX. " + onnx_input_name + " doesn't exist in the list of OpenVINO input tensor names");
}
size_t batch_slice_idx = 0;
if (subgraph_context_.has_dynamic_input_shape &&
global_context_.enable_dynamic_shapes == true &&
(global_context_.device_type.find("CPU") != std::string::npos ||
global_context_.device_type.find("GPU") != std::string::npos)) {
auto tensor = context.GetInput(subgraph_context_.input_names.at(input_name));
auto tensor_info = tensor.GetTensorTypeAndShapeInfo();
auto tensor_shape = tensor_info.GetShape();
auto tensor_size = tensor_shape.size();
auto tensor_iter = 0;
ov::Shape input_tensor_shape = ov::Shape(tensor_size, 0);
for (auto i = tensor_shape.begin(); i != tensor_shape.end(); ++i) {
input_tensor_shape[tensor_iter] = *i;
tensor_iter += 1;
}
auto input = ie_cnn_network_->get_parameters().at(input_idx);
OVTensorPtr tensor_ptr = std::make_shared<ov::Tensor>(input->get_element_type(), input_tensor_shape);
FillInputBlob(tensor_ptr, batch_slice_idx, input_name, context, subgraph_context_);
try {
infer_request->SetTensor(input_name, tensor_ptr);
} catch (const char* msg) {
throw(msg);
}
} else {
OVTensorPtr graph_input_blob;
try {
graph_input_blob = infer_request->GetTensor(input_name);
} catch (const char* msg) {
throw(msg);
}
FillInputBlob(graph_input_blob, batch_slice_idx, input_name, context, subgraph_context_);
}
input_idx++;
}
// Start Async inference
infer_request->StartAsync();
} catch (const char* msg) {
throw(msg);
}
}
#ifdef IO_BUFFER_ENABLED
// Wait for Remote Aynchronous inference completion
void BasicBackend::StartRemoteAsyncInference(Ort::KernelContext & context, OVInferRequestPtr infer_request) {
try {
auto graph_input_info = exe_network_.Get().inputs();
int input_idx = 0;
for (auto input_info_iter = graph_input_info.begin();
input_info_iter != graph_input_info.end(); ++input_info_iter) {
auto input_names = input_info_iter->get_names();
std::string onnx_input_name;
std::string input_name;
// use names retrieved from original ONNX model to assign the right onnx input name for the graph
for (auto it = subgraph_context_.input_names.begin(); it != subgraph_context_.input_names.end(); ++it) {
if (it->second == input_idx) {
onnx_input_name = it->first;
break;
}
}
// using the input name retrieved from ONNX original to match with the input names returned by OV tensors
if (input_names.find(onnx_input_name) != input_names.end()) {
input_name = onnx_input_name;
} else {
throw(log_tag + "Input names mismatch between OpenVINO and ONNX. " + onnx_input_name + " doesn't exist in the list of OpenVINO input tensor names");
}
input_idx++;
// Kernel Context Input Buffer
const auto tensor = context.GetInput(subgraph_context_.input_names.at(input_name));
// If the ORTValue wraps a device pointer
auto mem_info = tensor.GetTensorMemoryInfo();
if (mem_info.GetAllocatorName() == OpenVINO_GPU) {
// Get the shared buffer pointer
const void* tensor_data = tensor.GetTensorRawData();
const cl::Buffer* shared_buffer_const = static_cast<const cl::Buffer*>(tensor_data);
// Create an Input Remote Blob
auto input = ie_cnn_network_->get_parameters().at(0);
auto remote_blob = remote_context_->create_tensor(input->get_element_type(), input->get_shape(), *shared_buffer_const);
ov::Tensor tensor = static_cast<ov::Tensor>(remote_blob);
OVTensorPtr tensor_ptr = std::make_shared<ov::Tensor>(tensor);
infer_request->SetTensor(input_name, tensor_ptr);
} else {
OVTensorPtr graph_input_blob;
graph_input_blob = infer_request->GetTensor(input_name);
size_t batch_slice_idx = 0;
FillInputBlob(graph_input_blob, batch_slice_idx, input_name, context, subgraph_context_);
// Starts an asynchronous inference request for data in slice indexed by batch_slice_idx on
// an Infer Request indexed by infer_req_idx
void BasicBackend::StartAsyncInference(Ort::KernelContext& context, OVInferRequestPtr infer_request) {
try {
auto graph_input_info = exe_network_.Get().inputs();
int input_idx = 0;
for (auto input_info_iter = graph_input_info.begin();
input_info_iter != graph_input_info.end(); ++input_info_iter) {
auto input_names = input_info_iter->get_names();
std::string onnx_input_name;
std::string input_name;
// use names retrieved from original ONNX model to assign the right onnx input name for the graph
for (auto it = subgraph_context_.input_names.begin(); it != subgraph_context_.input_names.end(); ++it) {
if (it->second == input_idx) {
onnx_input_name = it->first;
break;
}
}
// Set the output blob as remote blob
auto graph_output_info = exe_network_.Get().outputs();
for (auto output_info_iter = graph_output_info.begin();
output_info_iter != graph_output_info.end(); ++output_info_iter) {
auto output_names = output_info_iter->get_names();
std::string onnx_output_name;
std::string output_name;
bool output_name_found = false;
// using the output name retrieved from ONNX original to match with the output names returned by OV tensors
for (auto it = subgraph_context_.output_names.begin(); it != subgraph_context_.output_names.end(); ++it) {
onnx_output_name = it->first;
if (output_names.find(onnx_output_name) != output_names.end()) {
// Assigning the output_name
output_name = it->first;
output_name_found = true;
break;
}
}
if (!output_name_found) {
throw std::string(log_tag + "Output names mismatch between OpenVINO and ONNX. [ONNX Output: ] " + onnx_output_name + " doesn't exist in the list of OpenVINO output tensor names");
}
size_t batch_size = 1;
auto tensor = GetOutputTensor(context, batch_size, infer_request, output_name, subgraph_context_.output_names);
auto mem_info = tensor.GetTensorMemoryInfo();
// Check if ORT Value wraps a device pointer
if (mem_info.GetAllocatorName() == OpenVINO_GPU) {
const void* tensor_data = tensor.GetTensorRawData();
const cl::Buffer* shared_buffer_const = static_cast<const cl::Buffer*>(tensor_data);
// Create a shared Blob, set the Infer Request Output Blob
auto output = ie_cnn_network_->get_results().at(0);
auto remote_tensor = remote_context_->create_tensor(output->get_element_type(), output->get_shape(), *shared_buffer_const);
ov::Tensor tensor = static_cast<ov::Tensor>(remote_tensor);
OVTensorPtr tensor_ptr = std::make_shared<ov::Tensor>(tensor);
try {
infer_request->SetTensor(output_name, tensor_ptr);
} catch (const char* msg) {
throw(msg);
}
}
// using the input name retrieved from ONNX original to match with the input names returned by OV tensors
if (input_names.find(onnx_input_name) != input_names.end()) {
input_name = onnx_input_name;
} else {
throw(log_tag + "Input names mismatch between OpenVINO and ONNX. " + onnx_input_name + " doesn't exist in the list of OpenVINO input tensor names");
}
// Start Async inference
infer_request->StartAsync();
} catch (const char* msg) {
throw(msg);
}
}
#endif
// Wait for asynchronous inference completion on an Infer Request object indexed by infer_req_idx
// and copy the results into a slice location within the batched output buffer indexed by batch_slice_idx
void BasicBackend::CompleteAsyncInference(Ort::KernelContext & context, OVInferRequestPtr infer_request) {
// Wait for Async inference completion
try {
infer_request->WaitRequest();
auto graph_output_info = exe_network_.Get().outputs();
for (auto output_info_iter = graph_output_info.begin();
output_info_iter != graph_output_info.end(); ++output_info_iter) {
OVTensorPtr graph_output_blob;
auto output_names = output_info_iter->get_names();
std::string onnx_output_name;
std::string output_name;
bool output_name_found = false;
// using the output name retrieved from ONNX original to match with the output names returned by OV tensors
for (auto it = subgraph_context_.output_names.begin(); it != subgraph_context_.output_names.end(); ++it) {
onnx_output_name = it->first;
if (output_names.find(onnx_output_name) != output_names.end()) {
// Assigning the output_name
output_name = it->first;
output_name_found = true;
break;
}
}
if (!output_name_found) {
throw(log_tag +
"Output names mismatch between OpenVINO and ONNX. "
"[ONNX Output: ] " +
onnx_output_name +
" doesn't exist in the "
"list of OpenVINO output tensor names");
size_t batch_slice_idx = 0;
if (subgraph_context_.has_dynamic_input_shape &&
global_context_.enable_dynamic_shapes == true &&
(global_context_.device_type.find("CPU") != std::string::npos ||
global_context_.device_type.find("GPU") != std::string::npos)) {
auto tensor = context.GetInput(subgraph_context_.input_names.at(input_name));
auto tensor_info = tensor.GetTensorTypeAndShapeInfo();
auto tensor_shape = tensor_info.GetShape();
auto tensor_size = tensor_shape.size();
auto tensor_iter = 0;
ov::Shape input_tensor_shape = ov::Shape(tensor_size, 0);
for (auto i = tensor_shape.begin(); i != tensor_shape.end(); ++i) {
input_tensor_shape[tensor_iter] = *i;
tensor_iter += 1;
}
auto input = ie_cnn_network_->get_parameters().at(input_idx);
OVTensorPtr tensor_ptr = std::make_shared<ov::Tensor>(input->get_element_type(), input_tensor_shape);
FillInputBlob(tensor_ptr, batch_slice_idx, input_name, context, subgraph_context_);
try {
graph_output_blob = infer_request->GetTensor(output_name);
infer_request->SetTensor(input_name, tensor_ptr);
} catch (const char* msg) {
throw(msg);
}
size_t batch_size = 1;
auto output_tensor = GetOutputTensor(context, batch_size, infer_request, output_name, subgraph_context_.output_names);
auto mem_info = output_tensor.GetTensorMemoryInfo();
if (mem_info.GetAllocatorName() == OpenVINO_GPU) {
return;
} else {
size_t batch_slice = 0;
FillOutputBlob(graph_output_blob, output_tensor, batch_slice);
} else {
OVTensorPtr graph_input_blob;
try {
graph_input_blob = infer_request->GetTensor(input_name);
} catch (const char* msg) {
throw(msg);
}
FillInputBlob(graph_input_blob, batch_slice_idx, input_name, context, subgraph_context_);
}
if (!const_outputs_map_.empty()) {
for (auto item : const_outputs_map_) {
auto out_name = item.first;
auto node = item.second;
auto output_tensor = GetOutputTensor(context, out_name, subgraph_context_.output_names, node);
auto mem_info = output_tensor.GetTensorMemoryInfo();
if (mem_info.GetAllocatorName() == OpenVINO_GPU) {
throw(log_tag + "IO Buffering is not supported for constant subgraphs");
} else {
FillOutputsWithConstantData(node, output_tensor);
}
}
}
} catch (const char* msg) {
throw(msg);
input_idx++;
}
// Start Async inference
infer_request->StartAsync();
} catch (const char* msg) {
throw(msg);
}
}
void BasicBackend::Infer(OrtKernelContext * ctx) {
// Preliminary Thread safety mechanism
// currently allows a maximum of 8 Infer request's to parallel execute at the same time
Ort::KernelContext context(ctx);
#ifdef IO_BUFFER_ENABLED
// Wait for Remote Aynchronous inference completion
void BasicBackend::StartRemoteAsyncInference(Ort::KernelContext& context, OVInferRequestPtr infer_request) {
try {
auto graph_input_info = exe_network_.Get().inputs();
int input_idx = 0;
for (auto input_info_iter = graph_input_info.begin();
input_info_iter != graph_input_info.end(); ++input_info_iter) {
auto input_names = input_info_iter->get_names();
std::string onnx_input_name;
std::string input_name;
// use names retrieved from original ONNX model to assign the right onnx input name for the graph
for (auto it = subgraph_context_.input_names.begin(); it != subgraph_context_.input_names.end(); ++it) {
if (it->second == input_idx) {
onnx_input_name = it->first;
break;
}
}
// using the input name retrieved from ONNX original to match with the input names returned by OV tensors
if (input_names.find(onnx_input_name) != input_names.end()) {
input_name = onnx_input_name;
} else {
throw(log_tag + "Input names mismatch between OpenVINO and ONNX. " + onnx_input_name + " doesn't exist in the list of OpenVINO input tensor names");
}
input_idx++;
// Kernel Context Input Buffer
const auto tensor = context.GetInput(subgraph_context_.input_names.at(input_name));
// If the ORTValue wraps a device pointer
auto mem_info = tensor.GetTensorMemoryInfo();
if (mem_info.GetAllocatorName() == OpenVINO_GPU) {
// Get the shared buffer pointer
const void* tensor_data = tensor.GetTensorRawData();
const cl::Buffer* shared_buffer_const = static_cast<const cl::Buffer*>(tensor_data);
// Create an Input Remote Blob
auto input = ie_cnn_network_->get_parameters().at(0);
auto remote_blob = remote_context_->create_tensor(input->get_element_type(), input->get_shape(), *shared_buffer_const);
ov::Tensor tensor = static_cast<ov::Tensor>(remote_blob);
OVTensorPtr tensor_ptr = std::make_shared<ov::Tensor>(tensor);
infer_request->SetTensor(input_name, tensor_ptr);
} else {
OVTensorPtr graph_input_blob;
graph_input_blob = infer_request->GetTensor(input_name);
size_t batch_slice_idx = 0;
FillInputBlob(graph_input_blob, batch_slice_idx, input_name, context, subgraph_context_);
}
}
LOGS_DEFAULT(INFO) << log_tag << "Running graph " << subgraph_context_.subgraph_name;
LOGS_DEFAULT(INFO) << log_tag << "In Infer";
// Set the output blob as remote blob
auto graph_output_info = exe_network_.Get().outputs();
for (auto output_info_iter = graph_output_info.begin();
output_info_iter != graph_output_info.end(); ++output_info_iter) {
auto output_names = output_info_iter->get_names();
std::string onnx_output_name;
std::string output_name;
bool output_name_found = false;
// using the output name retrieved from ONNX original to match with the output names returned by OV tensors
for (auto it = subgraph_context_.output_names.begin(); it != subgraph_context_.output_names.end(); ++it) {
onnx_output_name = it->first;
if (output_names.find(onnx_output_name) != output_names.end()) {
// Assigning the output_name
output_name = it->first;
output_name_found = true;
break;
}
}
if (!output_name_found) {
throw std::string(log_tag + "Output names mismatch between OpenVINO and ONNX. [ONNX Output: ] " + onnx_output_name + " doesn't exist in the list of OpenVINO output tensor names");
}
if (subgraph_context_.is_constant) {
size_t batch_size = 1;
auto tensor = GetOutputTensor(context, batch_size, infer_request, output_name, subgraph_context_.output_names);
auto mem_info = tensor.GetTensorMemoryInfo();
// Check if ORT Value wraps a device pointer
if (mem_info.GetAllocatorName() == OpenVINO_GPU) {
const void* tensor_data = tensor.GetTensorRawData();
const cl::Buffer* shared_buffer_const = static_cast<const cl::Buffer*>(tensor_data);
// Create a shared Blob, set the Infer Request Output Blob
auto output = ie_cnn_network_->get_results().at(0);
auto remote_tensor = remote_context_->create_tensor(output->get_element_type(), output->get_shape(), *shared_buffer_const);
ov::Tensor tensor = static_cast<ov::Tensor>(remote_tensor);
OVTensorPtr tensor_ptr = std::make_shared<ov::Tensor>(tensor);
try {
infer_request->SetTensor(output_name, tensor_ptr);
} catch (const char* msg) {
throw(msg);
}
}
}
// Start Async inference
infer_request->StartAsync();
} catch (const char* msg) {
throw(msg);
}
}
#endif
// Wait for asynchronous inference completion on an Infer Request object indexed by infer_req_idx
// and copy the results into a slice location within the batched output buffer indexed by batch_slice_idx
void BasicBackend::CompleteAsyncInference(Ort::KernelContext& context, OVInferRequestPtr infer_request) {
// Wait for Async inference completion
try {
infer_request->WaitRequest();
auto graph_output_info = exe_network_.Get().outputs();
for (auto output_info_iter = graph_output_info.begin();
output_info_iter != graph_output_info.end(); ++output_info_iter) {
OVTensorPtr graph_output_blob;
auto output_names = output_info_iter->get_names();
std::string onnx_output_name;
std::string output_name;
bool output_name_found = false;
// using the output name retrieved from ONNX original to match with the output names returned by OV tensors
for (auto it = subgraph_context_.output_names.begin(); it != subgraph_context_.output_names.end(); ++it) {
onnx_output_name = it->first;
if (output_names.find(onnx_output_name) != output_names.end()) {
// Assigning the output_name
output_name = it->first;
output_name_found = true;
break;
}
}
if (!output_name_found) {
throw(log_tag +
"Output names mismatch between OpenVINO and ONNX. "
"[ONNX Output: ] " +
onnx_output_name +
" doesn't exist in the "
"list of OpenVINO output tensor names");
}
try {
graph_output_blob = infer_request->GetTensor(output_name);
} catch (const char* msg) {
throw(msg);
}
size_t batch_size = 1;
auto output_tensor = GetOutputTensor(context, batch_size, infer_request, output_name, subgraph_context_.output_names);
auto mem_info = output_tensor.GetTensorMemoryInfo();
if (mem_info.GetAllocatorName() == OpenVINO_GPU) {
return;
} else {
size_t batch_slice = 0;
FillOutputBlob(graph_output_blob, output_tensor, batch_slice);
}
}
if (!const_outputs_map_.empty()) {
for (auto item : const_outputs_map_) {
auto out_name = item.first;
auto node = item.second;
try {
auto output_tensor = GetOutputTensor(context, out_name, subgraph_context_.output_names, node);
auto output_tensor = GetOutputTensor(context, out_name, subgraph_context_.output_names, node);
auto mem_info = output_tensor.GetTensorMemoryInfo();
if (mem_info.GetAllocatorName() == OpenVINO_GPU) {
throw(log_tag + "IO Buffering is not supported for constant subgraphs");
} else {
FillOutputsWithConstantData(node, output_tensor);
} catch (std::string const& msg) {
throw msg;
}
}
// Get Output tensors
LOGS_DEFAULT(INFO) << log_tag << "Inference successful";
// Enable CI Logs
if (IsCILogEnabled()) {
std::cout << "Inference successful" << std::endl;
}
}
} catch (const char* msg) {
throw(msg);
}
}
} else {
// Requesting for an idle infer_request from a pool of infer_requests_
OVInferRequestPtr infer_request;
infer_request = inferRequestsQueue_->getIdleRequest();
void BasicBackend::Infer(OrtKernelContext* ctx) {
// Preliminary Thread safety mechanism
// currently allows a maximum of 8 Infer request's to parallel execute at the same time
Ort::KernelContext context(ctx);
LOGS_DEFAULT(INFO) << log_tag << "Running graph " << subgraph_context_.subgraph_name;
LOGS_DEFAULT(INFO) << log_tag << "In Infer";
if (subgraph_context_.is_constant) {
for (auto item : const_outputs_map_) {
auto out_name = item.first;
auto node = item.second;
try {
auto output_tensor = GetOutputTensor(context, out_name, subgraph_context_.output_names, node);
FillOutputsWithConstantData(node, output_tensor);
} catch (std::string const& msg) {
throw msg;
}
}
// Get Output tensors
LOGS_DEFAULT(INFO) << log_tag << "Inference successful";
// Enable CI Logs
if (IsCILogEnabled()) {
std::cout << "Inference successful" << std::endl;
}
} else {
// Requesting for an idle infer_request from a pool of infer_requests_
OVInferRequestPtr infer_request;
infer_request = inferRequestsQueue_->getIdleRequest();
#ifdef IO_BUFFER_ENABLED
if ((global_context_.device_type.find("GPU") != std::string::npos) &&
(global_context_.context != nullptr) &&
(openvino_ep::BackendManager::GetGlobalContext().is_wholly_supported_graph)) {
try {
StartRemoteAsyncInference(context, infer_request);
} catch (std::string const& msg) {
throw msg;
}
} else {
try {
StartAsyncInference(context, infer_request);
} catch (std::string const& msg) {
throw msg;
}
if ((global_context_.device_type.find("GPU") != std::string::npos) &&
(global_context_.context != nullptr) &&
(openvino_ep::BackendManager::GetGlobalContext().is_wholly_supported_graph)) {
try {
StartRemoteAsyncInference(context, infer_request);
} catch (std::string const& msg) {
throw msg;
}
} else {
try {
StartAsyncInference(context, infer_request);
} catch (std::string const& msg) {
throw msg;
}
}
#else
try {
StartAsyncInference(context, infer_request);
@ -446,32 +433,32 @@ BasicBackend::BasicBackend(const ONNX_NAMESPACE::ModelProto& model_proto,
throw msg;
}
#endif
try {
CompleteAsyncInference(context, infer_request);
} catch (std::string const& msg) {
throw msg;
}
try {
CompleteAsyncInference(context, infer_request);
} catch (std::string const& msg) {
throw msg;
}
// Get Output tensors
LOGS_DEFAULT(INFO) << log_tag << "Inference successful";
// Enable CI Logs
if (IsCILogEnabled()) {
std::cout << "Inference successful" << std::endl;
}
// Get Output tensors
LOGS_DEFAULT(INFO) << log_tag << "Inference successful";
// Enable CI Logs
if (IsCILogEnabled()) {
std::cout << "Inference successful" << std::endl;
}
// Once the inference is completed, the infer_request becomes free and is placed back into pool of infer_requests_
inferRequestsQueue_->putIdleRequest(infer_request);
// Once the inference is completed, the infer_request becomes free and is placed back into pool of infer_requests_
inferRequestsQueue_->putIdleRequest(infer_request);
#ifndef NDEBUG
#ifndef IO_BUFFER_ENABLED // Printing performance counts is disabled when IO_BUFFER_ENABLED
if (openvino_ep::backend_utils::IsDebugEnabled()) {
inferRequestsQueue_->printstatus(); // Printing the elements of infer_requests_ vector pool only in debug mode
std::string& hw_target = (global_context_.device_id != "") ? global_context_.device_id : global_context_.device_type;
printPerformanceCounts(infer_request, std::cout, hw_target);
}
#endif
#endif
if (openvino_ep::backend_utils::IsDebugEnabled()) {
inferRequestsQueue_->printstatus(); // Printing the elements of infer_requests_ vector pool only in debug mode
std::string& hw_target = (global_context_.device_id != "") ? global_context_.device_id : global_context_.device_type;
printPerformanceCounts(infer_request, std::cout, hw_target);
}
#endif
#endif
}
}
} // namespace openvino_ep
} // namespace onnxruntime

2
onnxruntime/core/providers/openvino/ov_versions/capability.cc
View file

@ -42,7 +42,7 @@ std::vector<std::unique_ptr<ComputeCapability>> GetCapability::Execute() {
std::vector<std::unique_ptr<ComputeCapability>> result;
// Check if it is a subgraph
if (graph_viewer_.IsSubgraph()) {
if (graph_viewer_.IsSubgraph() && graph_viewer_.Name() == "tf2onnx") {
return result;
}

17
onnxruntime/core/providers/openvino/ov_versions/data_ops.cc
View file

@ -49,6 +49,7 @@ std::set<std::string> ops_supported_only_in_model = {
"OneHot",
"Pad",
"QuantizeLinear",
"RandomNormalLike",
"Range",
"ReduceMin",
"Resize",
@ -57,13 +58,14 @@ std::set<std::string> ops_supported_only_in_model = {
"Slice",
"Split",
"Tile",
"TopK"};
"TopK",
"Trilu"};
// Ops which are supported as functions (as composite ops)
std::set<std::string> ops_supported_as_function = {
"LessOrEqual",
"GreaterOrEqual",
};
"LayerNormalization"};
std::vector<SupportedOp> supported_op_mode = {
{"Abs", V_2020_4, {"CPU", "GPU"}},
@ -160,6 +162,7 @@ std::vector<SupportedOp> supported_op_mode = {
{"InstanceNormalization", V_2023_0, {"VPUX"}},
{"HardSigmoid", V_2020_4, {"CPU", "GPU"}},
{"HardMax", V_2022_1, {"CPU", "GPU"}},
{"LayerNormalization", V_2023_0, {"CPU", "GPU"}},
{"LeakyRelu", V_2020_4, {"CPU", "GPU"}},
{"LeakyRelu", V_2023_0, {"VPUX"}},
{"Less", V_2020_4, {"CPU", "GPU"}},
@ -205,6 +208,7 @@ std::vector<SupportedOp> supported_op_mode = {
{"QLinearMatMul", V_2022_3, {"CPU"}},
{"QuantizeLinear", V_2021_4, {"CPU", "GPU"}},
{"QuantizeLinear", V_2023_0, {"VPUX"}},
{"RandomNormalLike", V_2023_0, {"CPU", "GPU"}},
{"Range", V_2022_1, {"CPU", "GPU"}},
{"Range", V_2023_0, {"VPUX"}},
{"Reciprocal", V_2020_4, {"CPU", "GPU"}},
@ -278,6 +282,7 @@ std::vector<SupportedOp> supported_op_mode = {
{"Tile", V_2023_0, {"VPUX"}},
{"Transpose", V_2020_4, {"CPU", "GPU"}},
{"Transpose", V_2023_0, {"VPUX"}},
{"Trilu", V_2023_0, {"CPU", "GPU"}},
{"TopK", V_2020_4, {"CPU", "GPU"}},
{"TopK", V_2023_0, {"VPUX"}},
{"Unsqueeze", V_2020_4, {"CPU", "GPU"}},
@ -590,7 +595,7 @@ void DataOps::populate_op_mode_supported() {
op_list_.insert({"PRelu", obj});
}
{
UnsupportedOpMode obj = {{V_2022_1, V_2022_2, V_2022_3},
UnsupportedOpMode obj = {{V_2022_1, V_2022_2, V_2022_3, V_2023_0},
[this](const Node* node, const InitializedTensorSet&) {
const auto& input_arg = node->InputDefs()[1];
auto shape = input_arg->Shape();
@ -701,7 +706,7 @@ void DataOps::populate_op_mode_supported() {
op_list_.insert({"Squeeze", obj});
}
{
UnsupportedOpMode obj = {{V_2022_1, V_2022_2, V_2022_3},
UnsupportedOpMode obj = {{V_2022_1, V_2022_2, V_2022_3, V_2023_0},
[this](const Node* node, const InitializedTensorSet&) {
// If the operator is unsqueeze
// If axes is an input, then we cannot produce a static graph. Conversion fails in convert_function_to_cnn_network.
@ -715,7 +720,7 @@ void DataOps::populate_op_mode_supported() {
op_list_.insert({"Unsqueeze", obj});
}
{
UnsupportedOpMode obj = {{V_2022_1, V_2022_2, V_2022_3},
UnsupportedOpMode obj = {{V_2022_1, V_2022_2, V_2022_3, V_2023_0},
[this](const Node* node, const InitializedTensorSet&) {
// check for attributes
auto& upsample_attr = node->GetAttributes();
@ -732,7 +737,7 @@ void DataOps::populate_op_mode_supported() {
auto shape = x_arg->Shape();
if (shape != nullptr) {
// input tensor rank cannot be of one dimension
if (shape->dim_size() == 1) {
if (shape->dim_size() == 1 || shape->dim_size() == 4) {
return true;
}
}

2
onnxruntime/test/providers/cpu/controlflow/loop_test.cc
View file

@ -930,7 +930,7 @@ TEST(Loop, PassThroughSubgraphInputNoTypeOrShape) {
test.AddOutput<float>("loop_var_0_final", {1}, {123.f});
// Disable TensorRT on unsupported data type BOOL
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(Loop, BugFixIssue4031_implicit_input_handling) {

2
onnxruntime/test/providers/cpu/controlflow/scan_test.cc
View file

@ -25,7 +25,7 @@ struct RunOptions {
bool add_bad_shape = false;
bool mixed_execution_providers = false;
// Disable TensorRT because its parser fails, and it can't handle unknown dimensions
std::unordered_set<std::string> excluded_provider_types{kTensorrtExecutionProvider};
std::unordered_set<std::string> excluded_provider_types{kTensorrtExecutionProvider, kOpenVINOExecutionProvider};
};
static common::Status CreateSubgraph(Graph& graph, RunOptions& options, const std::string& failure_message = "");

6
onnxruntime/test/testdata/onnx_backend_test_series_filters.jsonc vendored
View file

@ -427,7 +427,11 @@
"^test_training_dropout_mask", // Runs but there's accuracy mismatch
"^test_training_dropout_default", // Runs but there's accuracy mismatch
"^test_center_crop_pad_crop_negative_axes_hwc*", // failed due to new types or shape infer with negative axis for CenterCropPad.
"^test_center_crop_pad_crop_negative_axes_hwc_expanded*" // failed due to new types or shape infer with negative axis for CenterCropPad.
"^test_center_crop_pad_crop_negative_axes_hwc_expanded*", // failed due to new types or shape infer with negative axis for CenterCropPad.
"test_range_int32_type_negative_delta_expanded_cpu", // Error but not a failure.
"test_range_float_type_positive_delta_expanded_cpu", // Error but not a failure.
"test_scan_sum_cpu", // Disabled due to output mismatch with tolerance.
"test_scan9_sum_cpu" // Disabled due to output mismatch with tolerance.
],
"current_failing_tests_OPENVINO_opset18": [
// pending opset 18 support, RUNTIME_EXCEPTION : Encountered unknown exception in Initialize()

35
tools/python/util/add_openvino_win_libs.py Normal file
View file

@ -0,0 +1,35 @@
# Copyright (C) 2022-2023 Intel Corporation
# Licensed under the MIT License
import os
import site
import sys
def add_openvino_libs_to_path() -> None:
"""Adds OpenVINO libraries to the PATH environment variable on Windows."""
if sys.platform == "win32":
# Installer, pip installs openvino dlls to the different directories
# and those paths need to be visible to the openvino-ep modules
#
# If you're using a custom installation of openvino,
# add the location of openvino dlls to your system PATH.
openvino_libs = []
# looking for the libs in the pip installation path.
if os.path.isdir(os.path.join(site.getsitepackages()[1], "openvino", "libs")):
openvino_libs.append(os.path.join(site.getsitepackages()[1], "openvino", "libs"))
else:
# setupvars.bat script set all libs paths to OPENVINO_LIB_PATHS environment variable.
openvino_libs_installer = os.getenv("OPENVINO_LIB_PATHS")
if openvino_libs_installer:
openvino_libs.extend(openvino_libs_installer.split(";"))
else:
sys.exit(
"Error: Please set the OPENVINO_LIB_PATHS environment variable. "
"If you use an install package, please, run setupvars.bat"
)
for lib in openvino_libs:
lib_path = os.path.join(os.path.dirname(__file__), lib)
if os.path.isdir(lib_path):
os.environ["PATH"] = os.path.abspath(lib_path) + ";" + os.environ["PATH"]
os.add_dll_directory(os.path.abspath(lib_path))