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OpenVINO-ExecutionProvider.md

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OpenVINO Execution Provider

OpenVINO Execution Provider enables deep learning inference on Intel CPUs, Intel integrated GPUs and Intel® MovidiusTM Vision Processing Units (VPUs). Please refer to this page for details on the Intel hardware supported.

Build

For build instructions, please see the BUILD page.

Runtime configuration options

OpenVINO EP can be configured with certain options at runtime that control the behavior of the EP. These options can be set as key-value pairs as below:-

Python API

Key-Value pairs for config options can be set using the Session.set_providers API as follows:-

session = onnxruntime.InferenceSession(<path_to_model_file>, options)
session.set_providers(['OpenVINOExecutionProvider'], [{Key1 : Value1, Key2 : Value2, ...}])

Note that this causes the InferenceSession to be re-initialized, which may cause model recompilation and hardware re-initialization

C/C++ API

All the options shown below are passed to SessionOptionsAppendExecutionProvider_OpenVINO() API and populated in the struct OrtOpenVINOProviderOptions in an example shown below, for example for CPU device type:-

OrtOpenVINOProviderOptions options;
options.device_type = "CPU_FP32";
options.enable_vpu_fast_compile = 0;
options.device_id = "";
options.num_of_threads = 8;
SessionOptionsAppendExecutionProvider_OpenVINO(session_options, &options);

Available configuration options

The following table lists all the available configuratoin optoins and the Key-Value pairs to set them:-

Key Key type Allowable Values Value type Description
device_type string CPU_FP32, GPU_FP32, GPU_FP16, MYRIAD_FP16, VAD-M_FP16, VAD-F_FP32, Any valid Hetero combination, Any valid Multi-Device combination string Overrides the accelerator hardware type and precision with these values at runtime. If this option is not explicitly set, default hardware and precision specified during build time is used.
device_id string Any valid OpenVINO device ID string Selects a particular hardware device for inference. The list of valid OpenVINO device ID's available on a platform can be obtained either by Python API (onnxruntime.capi._pybind_state.get_available_openvino_device_ids()) or by OpenVINO C/C++ API. If this option is not explicitly set, an arbitrary free device will be automatically selected by OpenVINO runtime.
enable_vpu_fast_compile string True/False boolean This option is only available for MYRIAD_FP16 VPU devices. During initialization of the VPU device with compiled model, Fast-compile may be optionally enabled to speeds up the model's compilation to VPU device specific format. This in-turn speeds up model initialization time. However, enabling this option may slowdown inference due to some of the optimizations not being fully applied, so caution is to be exercised while enabling this option.
num_of_threads string Any unsigned positive number other than 0 size_t Overrides the accelerator default value of number of threads with this value at runtime. If this option is not explicitly set, default value of 8 is used during build time.

Valid Hetero or Multi-Device combination's: HETERO:<DEVICE_TYPE_1>,<DEVICE_TYPE_2>,<DEVICE_TYPE_3>... MULTI:<DEVICE_TYPE_1>,<DEVICE_TYPE_2>,<DEVICE_TYPE_3>... The <DEVICE_TYPE> can be any of these devices from this list ['CPU','GPU','MYRIAD','FPGA','HDDL']

A minimum of two DEVICE_TYPE'S should be specified for a valid HETERO or Multi-Device Build.

Example: HETERO:MYRIAD,CPU HETERO:HDDL,GPU,CPU MULTI:MYRIAD,GPU,CPU

Other configuration settings

Onnxruntime Graph Optimization level

OpenVINO backend performs both hardware dependent as well as independent optimizations to the graph to infer it with on the target hardware with best possible performance. In most of the cases it has been observed that passing in the graph from the input model as is would lead to best possible optimizations by OpenVINO. For this reason, it is advised to turn off high level optimizations performed by ONNX Runtime before handing the graph over to OpenVINO backend. This can be done using Session options as shown below:-

Python API

options = onnxruntime.SessionOptions()
options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL
sess = onnxruntime.InferenceSession(<path_to_model_file>, options)

C/C++ API

SessionOptions::SetGraphOptimizationLevel(ORT_DISABLE_ALL);

Deprecated: Dynamic device type selection

Note: This API has been deprecated. Please use the mechanism mentioned above to set the 'device-type' option. When ONNX Runtime is built with OpenVINO Execution Provider, a target hardware option needs to be provided. This build time option becomes the default target harware the EP schedules inference on. However, this target may be overriden at runtime to schedule inference on a different hardware as shown below.

Note. This dynamic hardware selection is optional. The EP falls back to the build-time default selection if no dynamic hardware option value is specified.

Python API

import onnxruntime
onnxruntime.capi._pybind_state.set_openvino_device("<harware_option>")
# Create session after this

This property persists and gets applied to new sessions until it is explicity unset. To unset, assign a null string ("").

C/C++ API

Append the settings string "<hardware_option>" to the EP settings string. Example shown below for the CPU_FP32 option:

std::string settings_str;
...
settings_str.append("CPU_FP32");
Ort::ThrowOnError(OrtSessionOptionsAppendExecutionProvider_OpenVINO(sf, settings_str.c_str()));

ONNX Layers supported using OpenVINO

The table below shows the ONNX layers supported and validated using OpenVINO Execution Provider.The below table also lists the Intel hardware support for each of the layers. CPU refers to Intel® Atom, Core, and Xeon processors. GPU refers to the Intel Integrated Graphics. VPU refers to USB based Intel® MovidiusTM VPUs as well as Intel® Vision accelerator Design with Intel Movidius TM MyriadX VPU.

ONNX Layers CPU GPU VPU
Abs Yes Yes No
Acos Yes No No
Acosh Yes No No
Add Yes Yes Yes
ArgMax Yes No No
ArgMin Yes No No
Asin Yes Yes No
Asinh Yes Yes No
Atan Yes Yes No
Atanh Yes No No
AveragePool Yes Yes Yes
BatchNormalization Yes Yes Yes
Cast Yes Yes Yes
Clip Yes Yes Yes
Concat Yes Yes Yes
Constant Yes Yes Yes
ConstantOfShape Yes Yes Yes
Conv Yes Yes Yes
ConvTranspose Yes Yes Yes
Cos Yes No No
Cosh Yes No No
DepthToSpace Yes Yes Yes
Div Yes Yes Yes
Dropout Yes Yes Yes
Elu Yes Yes Yes
Equal Yes Yes Yes
Erf Yes Yes Yes
Exp Yes Yes Yes
Expand No No Yes
Flatten Yes Yes Yes
Floor Yes Yes Yes
Gather Yes Yes Yes
Gemm Yes Yes Yes
GlobalAveragePool Yes Yes Yes
GlobalLpPool Yes Yes No
HardSigmoid Yes Yes No
Identity Yes Yes Yes
InstanceNormalization Yes Yes Yes
LeakyRelu Yes Yes Yes
Less Yes Yes Yes
Log Yes Yes Yes
LRN Yes Yes Yes
MatMul Yes Yes Yes
Max Yes Yes Yes
MaxPool Yes Yes Yes
Mean Yes Yes Yes
Min Yes Yes Yes
Mul Yes Yes Yes
Neg Yes Yes Yes
NonMaxSuppression No No Yes
NonZero Yes No Yes
Not Yes Yes No
OneHot Yes Yes Yes
Pad Yes Yes Yes
Pow Yes Yes Yes
PRelu Yes Yes Yes
Reciprocal Yes Yes Yes
ReduceLogSum Yes No Yes
ReduceMax Yes Yes Yes
ReduceMean Yes Yes Yes
ReduceMin Yes Yes Yes
ReduceProd Yes No No
ReduceSum Yes Yes Yes
ReduceSumSquare Yes No Yes
Relu Yes Yes Yes
Reshape Yes Yes Yes
Resize Yes No Yes
RoiAlign No No Yes
Scatter No No Yes
Selu Yes Yes No
Shape Yes Yes Yes
Sigmoid Yes Yes Yes
Sign Yes No No
SinFloat No No Yes
Sinh Yes No No
Slice Yes Yes Yes
Softmax Yes Yes Yes
Softsign Yes No No
SpaceToDepth Yes Yes Yes
Split Yes Yes Yes
Sqrt Yes Yes Yes
Squeeze Yes Yes Yes
Sub Yes Yes Yes
Sum Yes Yes Yes
Tan Yes Yes No
Tanh Yes Yes Yes
TopK Yes Yes Yes
Transpose Yes Yes Yes
Unsqueeze Yes Yes Yes

Topology Support

Below topologies from ONNX open model zoo are fully supported on OpenVINO Execution Provider and many more are supported through sub-graph partitioning

Image Classification Networks

MODEL NAME CPU GPU VPU FPGA
bvlc_alexnet Yes Yes Yes Yes*
bvlc_googlenet Yes Yes Yes Yes*
bvlc_reference_caffenet Yes Yes Yes Yes*
bvlc_reference_rcnn_ilsvrc13 Yes Yes Yes Yes*
emotion ferplus Yes Yes Yes Yes*
densenet121 Yes Yes Yes Yes*
inception_v1 Yes Yes Yes Yes*
inception_v2 Yes Yes Yes Yes*
mobilenetv2 Yes Yes Yes Yes*
resnet18v1 Yes Yes Yes Yes*
resnet34v1 Yes Yes Yes Yes*
resnet101v1 Yes Yes Yes Yes*
resnet152v1 Yes Yes Yes Yes*
resnet18v2 Yes Yes Yes Yes*
resnet34v2 Yes Yes Yes Yes*
resnet101v2 Yes Yes Yes Yes*
resnet152v2 Yes Yes Yes Yes*
resnet50 Yes Yes Yes Yes*
resnet50v2 Yes Yes Yes Yes*
shufflenet Yes Yes Yes Yes*
squeezenet1.1 Yes Yes Yes Yes*
vgg19 Yes Yes Yes Yes*
vgg16 Yes Yes Yes Yes*
zfnet512 Yes Yes Yes Yes*
arcface Yes Yes Yes Yes*

Image Recognition Networks

MODEL NAME CPU GPU VPU FPGA
mnist Yes Yes Yes Yes*

Object Detection Networks

MODEL NAME CPU GPU VPU FPGA
tiny_yolov2 Yes Yes Yes Yes*

Image Manipulation Networks

MODEL NAME CPU GPU VPU FPGA
mosaic Yes No No No*
candy Yes No No No*
rain_princess Yes No No No*
pointilism Yes No No No*
udnie Yes No No No*

*FPGA only runs in HETERO mode wherein the layers that are not supported on FPGA fall back to OpenVINO CPU.

CSharp API

To use csharp api for openvino execution provider create a custom nuget package. Follow the instructions here to install prerequisites for nuget creation. Once prerequisites are installed follow the instructions to build openvino and add an extra flag --build_nuget to create nuget packages. Two nuget packages will be created Microsoft.ML.OnnxRuntime.Managed and Microsoft.ML.OnnxRuntime.Openvino.

Multi-threading for OpenVINO EP

OpenVINO Execution Provider enables thread-safe deep learning inference

Heterogeneous Execution for OpenVINO EP

The heterogeneous Execution enables computing for inference on one network on several devices. Purposes to execute networks in heterogeneous mode

To utilize accelerators power and calculate heaviest parts of network on accelerator and execute not supported layers on fallback devices like CPU To utilize all available hardware more efficiently during one inference

For more information on Heterogeneous plugin of OpenVINO, please refer to the following documentation.

Multi-Device Execution for OpenVINO EP

Multi-Device plugin automatically assigns inference requests to available computational devices to execute the requests in parallel. Potential gains are as follows

Improved throughput that multiple devices can deliver (compared to single-device execution) More consistent performance, since the devices can now share the inference burden (so that if one device is becoming too busy, another device can take more of the load)

For more information on Multi-Device plugin of OpenVINO, please refer to the following documentation.