Asynchronous Inference Request — OpenVINO™ documentation (original) (raw)

Asynchronous Inference Request runs an inference pipeline asynchronously in one or several task executors depending on a device pipeline structure. OpenVINO Runtime Plugin API provides the base ov::IAsyncInferRequest class:

• The class has the m_pipeline field of std::vector<std::pair<std::shared_ptr<ov::threading::ITaskExecutor>, ov::threading::Task> >, which contains pairs of an executor and executed task.
• All executors are passed as arguments to a class constructor and they are in the running state and ready to run tasks.
• The class has the ov::IAsyncInferRequest::stop_and_wait method, which waits for m_pipeline to finish in a class destructor. The method does not stop task executors and they are still in the running stage, because they belong to the compiled model instance and are not destroyed.

AsyncInferRequest Class#

OpenVINO Runtime Plugin API provides the base ov::IAsyncInferRequest class for a custom asynchronous inference request implementation:

class AsyncInferRequest : public ov::IAsyncInferRequest { public: AsyncInferRequest(const std::shared_ptr& request, const std::shared_ptrov::threading::ITaskExecutor& task_executor, const std::shared_ptrov::threading::ITaskExecutor& wait_executor, const std::shared_ptrov::threading::ITaskExecutor& callback_executor);

~AsyncInferRequest();
void cancel() override;

private: std::function<void()> m_cancel_callback; std::shared_ptrov::threading::ITaskExecutor m_wait_executor; };

Class Fields#

• m_cancel_callback - a callback which allows to interrupt the execution
• m_wait_executor - a task executor that waits for a response from a device about device tasks completion

Note

If a plugin can work with several instances of a device, m_wait_executor must be device-specific. Otherwise, having a single task executor for several devices does not allow them to work in parallel.

AsyncInferRequest()#

The main goal of the AsyncInferRequest constructor is to define a device pipeline m_pipeline. The example below demonstrates m_pipeline creation with the following stages:

• infer_preprocess_and_start_pipeline is a CPU lightweight task to submit tasks to a remote device.
• wait_pipeline is a CPU non-compute task that waits for a response from a remote device.
• infer_postprocess is a CPU compute task.

ov::template_plugin::AsyncInferRequest::AsyncInferRequest( const std::shared_ptrov::template_plugin::InferRequest& request, const std::shared_ptrov::threading::ITaskExecutor& task_executor, const std::shared_ptrov::threading::ITaskExecutor& wait_executor, const std::shared_ptrov::threading::ITaskExecutor& callback_executor) : ov::IAsyncInferRequest(request, task_executor, callback_executor), m_wait_executor(wait_executor) { // In current implementation we have CPU only tasks and no needs in 2 executors // So, by default single stage pipeline is created. // This stage executes InferRequest::infer() using cpuTaskExecutor. // But if remote asynchronous device is used the pipeline can by splitted tasks that are executed by cpuTaskExecutor // and waiting tasks. Waiting tasks can lock execution thread so they use separate threads from other executor. constexpr const auto remoteDevice = false;

m_cancel_callback = [request] {
    request->cancel();
};
if (remoteDevice) {
    m_pipeline = {{task_executor,
                   [this, request] {
                       OV_ITT_SCOPED_TASK(itt::domains::TemplatePlugin,
                                          "TemplatePlugin::AsyncInferRequest::infer_preprocess_and_start_pipeline");
                       request->infer_preprocess();
                       request->start_pipeline();
                   }},
                  {m_wait_executor,
                   [this, request] {
                       OV_ITT_SCOPED_TASK(itt::domains::TemplatePlugin,
                                          "TemplatePlugin::AsyncInferRequest::wait_pipeline");
                       request->wait_pipeline();
                   }},
                  {task_executor, [this, request] {
                       OV_ITT_SCOPED_TASK(itt::domains::TemplatePlugin,
                                          "TemplatePlugin::AsyncInferRequest::infer_postprocess");
                       request->infer_postprocess();
                   }}};
}

}

The stages are distributed among two task executors in the following way:

• infer_preprocess_and_start_pipeline prepare input tensors and run on m_request_executor, which computes CPU tasks.
• You need at least two executors to overlap compute tasks of a CPU and a remote device the plugin works with. Otherwise, CPU and device tasks are executed serially one by one.
• wait_pipeline is sent to m_wait_executor, which works with the device.

Note

m_callback_executor is also passed to the constructor and it is used in the base ov::IAsyncInferRequest class, which adds a pair of callback_executor and a callback function set by the user to the end of the pipeline.

~AsyncInferRequest()#

In the asynchronous request destructor, it is necessary to wait for a pipeline to finish. It can be done using the ov::IAsyncInferRequest::stop_and_wait method of the base class.

ov::template_plugin::AsyncInferRequest::~AsyncInferRequest() { ov::IAsyncInferRequest::stop_and_wait(); }

cancel()#

The method allows to cancel the infer request execution:

void ov::template_plugin::AsyncInferRequest::cancel() { ov::IAsyncInferRequest::cancel(); m_cancel_callback(); }