# Plugin {#plugin}

Inference Engine Plugin usually represents a wrapper around a backend. Backends can be:
- OpenCL-like backend (e.g. clDNN library) for GPU devices.
- MKLDNN backend for Intel CPU devices.
- NVIDIA cuDNN for NVIDIA GPUs.

The responsibility of Inference Engine Plugin:
- Initializes a backend and throw exception in `Engine` constructor if backend cannot be initialized.
- Provides information about devices enabled by a particular backend, e.g. how many devices, their properties and so on.
- Loads or imports [executable network](@ref executable_network) objects.

In addition to the Inference Engine Public API, the Inference Engine provides the Plugin API, which is a set of functions and helper classes that simplify new plugin development:

- header files in the `inference_engine/src/plugin_api` directory
- implementations in the `inference_engine/src/inference_engine` directory
- symbols in the Inference Engine Core shared library

To build an Inference Engine plugin with the Plugin API, see the [Inference Engine Plugin Building](@ref plugin_build) guide.  

Plugin Class
------------------------

Inference Engine Plugin API provides the helper InferenceEngine::InferencePluginInternal class recommended to use as a base class for a plugin.
Based on that, declaration of a plugin class can look as follows:

@snippet src/template_plugin.hpp plugin:header

#### Class Fields

The provided plugin class also has several fields:

* `_backend` - a backend engine that is used to perform actual computations for network inference. For `Template` plugin `ngraph::runtime::Backend` is used which performs computations using ngraph reference implementations.
* `_waitExecutor` - a task executor that waits for a response from a device about device tasks completion.
* `_cfg` of type `Configuration`:

@snippet src/template_config.hpp configuration:header

As an example, a plugin configuration has three value parameters:

- `deviceId` - particular device ID to work with. Applicable if a plugin supports more than one `Template` device. In this case, some plugin methods, like `SetConfig`, `QueryNetwork`, and `LoadNetwork`, must support the CONFIG_KEY(KEY_DEVICE_ID) parameter. 
- `perfCounts` - boolean value to identify whether to collect performance counters during [Inference Request](@ref infer_request) execution.
- `_streamsExecutorConfig` - configuration of `InferenceEngine::IStreamsExecutor` to handle settings of multi-threaded context.

### Engine Constructor

A plugin constructor must contain code that checks the ability to work with a device of the `Template` 
type. For example, if some drivers are required, the code must check 
driver availability. If a driver is not available (for example, OpenCL runtime is not installed in 
case of a GPU device or there is an improper version of a driver is on a host machine), an exception 
must be thrown from a plugin constructor.

A plugin must define a device name enabled via the `_pluginName` field of a base class:

@snippet src/template_plugin.cpp plugin:ctor

### `LoadExeNetworkImpl()`

**Implementation details:** The base InferenceEngine::InferencePluginInternal class provides a common implementation 
of the public InferenceEngine::InferencePluginInternal::LoadNetwork method that calls plugin-specific `LoadExeNetworkImpl`, which is defined in a derived class.

This is the most important function of the `Plugin` class and creates an instance of compiled `ExecutableNetwork`,
which holds a backend-dependent compiled graph in an internal representation:

@snippet src/template_plugin.cpp plugin:load_exe_network_impl

Before a creation of an `ExecutableNetwork` instance via a constructor, a plugin may check if a provided 
InferenceEngine::ICNNNetwork object is supported by a device. In the example above, the plugin checks precision information.

The very important part before creation of `ExecutableNetwork` instance is to call `TransformNetwork` method which applies ngraph transformation passes.

Actual graph compilation is done in the `ExecutableNetwork` constructor. Refer to the [ExecutableNetwork Implementation Guide](@ref executable_network) for details.

> **NOTE**: Actual configuration map used in `ExecutableNetwork` is constructed as a base plugin 
> configuration set via `Plugin::SetConfig`, where some values are overwritten with `config` passed to `Plugin::LoadExeNetworkImpl`. 
> Therefore, the config of  `Plugin::LoadExeNetworkImpl` has a higher priority.

### `TransformNetwork()`

The function accepts a const shared pointer to `ngraph::Function` object and performs the following steps:

1. Deep copies a const object to a local object, which can later be modified.
2. Applies common and plugin-specific transformations on a copied graph to make the graph more friendly to hardware operations. For details how to write custom plugin-specific transformation, please, refer to [Writing ngraph transformations](@ref ngraph_transformation) guide. See detailed topics about network representation:
    * [Intermediate Representation and Operation Sets](../_docs_MO_DG_IR_and_opsets.html)
    * [Quantized networks](@ref quantized_networks).

@snippet src/template_plugin.cpp plugin:transform_network

> **NOTE**: After all these transformations, a `ngraph::Function` object cointains operations which can be perfectly mapped to backend kernels. E.g. if backend has kernel computing `A + B` operations at once, the `TransformNetwork` function should contain a pass which fuses operations `A` and `B` into a single custom operation `A + B` which fits backend kernels set. 

### `QueryNetwork()`

Use the method with the `HETERO` mode, which allows to distribute network execution between different 
devices based on the `ngraph::Node::get_rt_info()` map, which can contain the `"affinity"` key.
The `QueryNetwork` method analyzes operations of provided `network` and returns a list of supported
operations via the InferenceEngine::QueryNetworkResult structure. The `QueryNetwork` firstly applies `TransformNetwork` passes to input `ngraph::Function` argument. After this, the transformed network in ideal case contains only operations are 1:1 mapped to kernels in computational backend. In this case, it's very easy to analyze which operations is supposed (`_backend` has a kernel for such operation or extensions for the operation is provided) and not supported (kernel is missed in `_backend`):

1. Store original names of all operations in input `ngraph::Function`
2. Apply `TransformNetwork` passes. Note, the names of operations in a transformed network can be different and we need to restore the mapping in the steps below.
3. Construct `supported` and `unsupported` maps which contains names of original operations. Note, that since the inference is performed using ngraph reference backend, the decision whether the operation is supported or not depends on whether the latest OpenVINO opset contains such operation.
4. `QueryNetworkResult.supportedLayersMap` contains only operations which are fully supported by `_backend`.

@snippet src/template_plugin.cpp plugin:query_network

### `AddExtension()`

Adds an extension of the InferenceEngine::IExtensionPtr type to a plugin. If a plugin does not 
support extensions, the method must throw an exception:

@snippet src/template_plugin.cpp plugin:add_extension

### `SetConfig()`

Sets new values for plugin configuration keys:

@snippet src/template_plugin.cpp plugin:set_config

In the snippet above, the `Configuration` class overrides previous configuration values with the new 
ones. All these values are used during backend specific graph compilation and execution of inference requests.

> **NOTE**: The function must throw an exception if it receives an unsupported configuration key.

### `GetConfig()`

Returns a current value for a specified configuration key:

@snippet src/template_plugin.cpp plugin:get_config

The function is implemented with the `Configuration::Get` method, which wraps an actual configuration 
key value to the InferenceEngine::Parameter and returns it.

> **NOTE**: The function must throw an exception if it receives an unsupported configuration key.

### `GetMetric()`

Returns a metric value for a metric with the name `name`. A device metric is a static type of information 
from a plugin about its devices or device capabilities. 

Examples of metrics:

- METRIC_KEY(AVAILABLE_DEVICES) - list of available devices that are required to implement. In this case, you can use 
all devices of the same `Template` type with automatic logic of the `MULTI` device plugin.
- METRIC_KEY(FULL_DEVICE_NAME) - full device name. In this case, a particular device ID is specified 
in the `option` parameter as `{ CONFIG_KEY(KEY_DEVICE_ID), "deviceID" }`.
- METRIC_KEY(SUPPORTED_METRICS) - list of metrics supported by a plugin
- METRIC_KEY(SUPPORTED_CONFIG_KEYS) - list of configuration keys supported by a plugin that
affects their behavior during a backend specific graph compilation or an inference requests execution
- METRIC_KEY(OPTIMIZATION_CAPABILITIES) - list of optimization capabilities of a device.
For example, supported data types and special optimizations for them.
- Any other device-specific metrics. In this case, place metrics declaration and possible values to 
a plugin-specific public header file, for example, `template/template_config.hpp`. The example below 
demonstrates the definition of a new optimization capability value specific for a device:

@snippet template/template_config.hpp public_header:metrics 

The snippet below provides an example of the implementation for `GetMetric`:

@snippet src/template_plugin.cpp plugin:get_metric

> **NOTE**: If an unsupported metric key is passed to the function, it must throw an exception.

### `ImportNetworkImpl()`

The importing network mechanism allows to import a previously exported backend specific graph and wrap it 
using an [ExecutableNetwork](@ref executable_network) object. This functionality is useful if 
backend specific graph compilation takes significant time and/or cannot be done on a target host 
device due to other reasons.

**Implementation details:** The base plugin class InferenceEngine::InferencePluginInternal implements InferenceEngine::InferencePluginInternal::ImportNetwork 
as follows: exports a device type (InferenceEngine::InferencePluginInternal::_pluginName) and then calls `ImportNetworkImpl`, 
which is implemented in a derived class. 
If a plugin cannot use the base implementation InferenceEngine::InferencePluginInternal::ImportNetwork, it can override base 
implementation and define an output blob structure up to its needs. This 
can be useful if a plugin exports a blob in a special format for integration with other frameworks 
where a common Inference Engine header from a base class implementation is not appropriate. 

During export of backend specific graph using `ExecutableNetwork::Export`, a plugin may export any 
type of information it needs to import a compiled graph properly and check its correctness. 
For example, the export information may include:

- Compilation options (state of `Plugin::_cfg` structure)
- Information about a plugin and a device type to check this information later during the import and 
throw an exception if the `model` stream contains wrong data. For example, if devices have different 
capabilities and a graph compiled for a particular device cannot be used for another, such type of 
information must be stored and checked during the import. 
- Compiled backend specific graph itself
- Information about precisions and shapes set by the user

@snippet src/template_plugin.cpp plugin:import_network_impl

Create Instance of Plugin Class
------------------------

Inference Engine plugin library must export only one function creating a plugin instance using IE_DEFINE_PLUGIN_CREATE_FUNCTION macro:

@snippet src/template_plugin.cpp plugin:create_plugin_engine

Next step in a plugin library implementation is the [ExecutableNetwork](@ref executable_network) class.
