Remote Tensor API of GPU Plugin

The GPU plugin implementation of the ov::RemoteContext and ov::RemoteTensor interfaces supports GPU pipeline developers who need video memory sharing and interoperability with existing native APIs, such as OpenCL, Microsoft DirectX, or VAAPI.

The ov::RemoteContext and ov::RemoteTensor interface implementation targets the need for memory sharing and interoperability with existing native APIs, such as OpenCL, Microsoft DirectX, and VAAPI. They allow you to avoid any memory copy overhead when plugging OpenVINO™ inference into an existing GPU pipeline. They also enable OpenCL kernels to participate in the pipeline to become native buffer consumers or producers of the OpenVINO™ inference.

There are two interoperability scenarios supported by the Remote Tensor API:

• The GPU plugin context and memory objects can be constructed from low-level device, display, or memory handles and used to create the OpenVINO™ ov::CompiledModel or ov::Tensor objects.

• The OpenCL context or buffer handles can be obtained from existing GPU plugin objects, and used in OpenCL processing on the application side.

Class and function declarations for the API are defined in the following files:

• Windows openvino/runtime/intel_gpu/ocl/ocl.hpp and openvino/runtime/intel_gpu/ocl/dx.hpp

• Linux openvino/runtime/intel_gpu/ocl/ocl.hpp and openvino/runtime/intel_gpu/ocl/va.hpp

The most common way to enable the interaction of your application with the Remote Tensor API is to use user-side utility classes and functions that consume or produce native handles directly.

Context Sharing Between Application and GPU Plugin

GPU plugin classes that implement the ov::RemoteContext interface are responsible for context sharing. Obtaining a context object is the first step in sharing pipeline objects. The context object of the GPU plugin directly wraps OpenCL context, setting a scope for sharing the ov::CompiledModel and ov::RemoteTensor objects. The ov::RemoteContext object can be either created on top of an existing handle from a native API or retrieved from the GPU plugin.

Once you have obtained the context, you can use it to compile a new ov::CompiledModel or create ov::RemoteTensor objects. For network compilation, use a dedicated flavor of ov::Core::compile_model(), which accepts the context as an additional parameter.

Creation of RemoteContext from Native Handle

To create the ov::RemoteContext object for user context, explicitly provide the context to the plugin using constructor for one of ov::RemoteContext derived classes.

Linux

Create from cl_context

    cl_context ctx = get_cl_context();
    ov::intel_gpu::ocl::ClContext gpu_context(core, ctx);

Create from cl_queue

    cl_command_queue queue = get_cl_queue();
    ov::intel_gpu::ocl::ClContext gpu_context(core, queue);

Create from VADisplay

    VADisplay display = get_va_display();
    ov::intel_gpu::ocl::VAContext gpu_context(core, display);

Windows

Create from cl_context

    cl_context ctx = get_cl_context();
    ov::intel_gpu::ocl::ClContext gpu_context(core, ctx);

Create from cl_queue

    cl_command_queue queue = get_cl_queue();
    ov::intel_gpu::ocl::ClContext gpu_context(core, queue);

Create from ID3D11Device

    ID3D11Device* device = get_d3d_device();
    ov::intel_gpu::ocl::D3DContext gpu_context(core, device);

Getting RemoteContext from the Plugin

If you do not provide any user context, the plugin uses its default internal context. The plugin attempts to use the same internal context object as long as plugin options are kept the same. Therefore, all ov::CompiledModel objects created during this time share the same context. Once the plugin options have been changed, the internal context is replaced by the new one.

To request the current default context of the plugin, use one of the following methods:

Get context from Core

    auto gpu_context = core.get_default_context("GPU").as<ov::intel_gpu::ocl::ClContext>();
    // Extract ocl context handle from RemoteContext
    cl_context context_handle = gpu_context.get();

Get context from compiled model

    auto gpu_context = compiled_model.get_context().as<ov::intel_gpu::ocl::ClContext>();
    // Extract ocl context handle from RemoteContext
    cl_context context_handle = gpu_context.get();

Memory Sharing Between Application and GPU Plugin

The classes that implement the ov::RemoteTensor interface are the wrappers for native API memory handles (which can be obtained from them at any time).

To create a shared tensor from a native memory handle, use dedicated create_tensor or create_tensor_nv12 methods of the ov::RemoteContext sub-classes. ov::intel_gpu::ocl::ClContext has multiple overloads of create_tensor methods which allow to wrap pre-allocated native handles with the ov::RemoteTensor object or request plugin to allocate specific device memory. For more details, see the code snippets below:

Wrap native handles

USM pointer

    void* shared_buffer = allocate_usm_buffer(input_size);
    auto remote_tensor = gpu_context.create_tensor(in_element_type, in_shape, shared_buffer);

cl_mem

    cl_mem shared_buffer = allocate_cl_mem(input_size);
    auto remote_tensor = gpu_context.create_tensor(in_element_type, in_shape, shared_buffer);

cl::Buffer

    cl::Buffer shared_buffer = allocate_buffer(input_size);
    auto remote_tensor = gpu_context.create_tensor(in_element_type, in_shape, shared_buffer);

cl::Image2D

    cl::Image2D shared_buffer = allocate_image(input_size);
    auto remote_tensor = gpu_context.create_tensor(in_element_type, in_shape, shared_buffer);

biplanar NV12 surface

    cl::Image2D y_plane_surface = allocate_image(y_plane_size);
    cl::Image2D uv_plane_surface = allocate_image(uv_plane_size);
    auto remote_tensor = gpu_context.create_tensor_nv12(y_plane_surface, uv_plane_surface);
    auto y_tensor = remote_tensor.first;
    auto uv_tensor = remote_tensor.second;

Allocate device memory

USM host memory

    ov::intel_gpu::ocl::USMTensor remote_tensor = gpu_context.create_usm_host_tensor(in_element_type, in_shape);
    // Extract raw usm pointer from remote tensor
    void* usm_ptr = remote_tensor.get();

USM device memory

    auto remote_tensor = gpu_context.create_usm_device_tensor(in_element_type, in_shape);
    // Extract raw usm pointer from remote tensor
    void* usm_ptr = remote_tensor.get();

cl::Buffer

    ov::RemoteTensor remote_tensor = gpu_context.create_tensor(in_element_type, in_shape);
    // Cast from base to derived class and extract ocl memory handle
    auto buffer_tensor = remote_tensor.as<ov::intel_gpu::ocl::ClBufferTensor>();
    cl_mem handle = buffer_tensor.get();

The ov::intel_gpu::ocl::D3DContext and ov::intel_gpu::ocl::VAContext classes are derived from ov::intel_gpu::ocl::ClContext. Therefore, they provide the functionality described above and extend it to allow creation of ov::RemoteTensor objects from ID3D11Buffer, ID3D11Texture2D pointers or the VASurfaceID handle respectively.

Direct NV12 Video Surface Input

To support the direct consumption of a hardware video decoder output, the GPU plugin accepts:

• Two-plane NV12 video surface input - calling the create_tensor_nv12() function creates a pair of ov::RemoteTensor objects, representing the Y and UV planes.

• Single-plane NV12 video surface input - calling the create_tensor() function creates one ov::RemoteTensor object, representing the Y and UV planes at once (Y elements before UV elements).

• NV12 to Grey video surface input conversion - calling the create_tensor() function creates one ov::RemoteTensor object, representing only the Y plane.

To ensure that the plugin generates a correct execution graph, static preprocessing should be added before model compilation:

two-plane

    using namespace ov::preprocess;
    auto p = PrePostProcessor(model);
    p.input().tensor().set_element_type(ov::element::u8)
                      .set_color_format(ov::preprocess::ColorFormat::NV12_TWO_PLANES, {"y", "uv"})
                      .set_memory_type(ov::intel_gpu::memory_type::surface);
    p.input().preprocess().convert_color(ov::preprocess::ColorFormat::BGR);
    p.input().model().set_layout("NCHW");
    auto model_with_preproc = p.build();

single-plane

    using namespace ov::preprocess;
    auto p = PrePostProcessor(model);
    p.input().tensor().set_element_type(ov::element::u8)
                      .set_color_format(ColorFormat::NV12_SINGLE_PLANE)
                      .set_memory_type(ov::intel_gpu::memory_type::surface);
    p.input().preprocess().convert_color(ov::preprocess::ColorFormat::BGR);
    p.input().model().set_layout("NCHW");
    auto model_with_preproc = p.build();

NV12 to Grey

    using namespace ov::preprocess;
    auto p = PrePostProcessor(model);
    p.input().tensor().set_element_type(ov::element::u8)
                      .set_layout("NHWC")
                      .set_memory_type(ov::intel_gpu::memory_type::surface);
    p.input().model().set_layout("NCHW");
    auto model_with_preproc = p.build();

Since the ov::intel_gpu::ocl::ClImage2DTensor and its derived classes do not support batched surfaces, if batching and surface sharing are required at the same time, inputs need to be set via the ov::InferRequest::set_tensors method with vector of shared surfaces for each plane:

Single Batch

two-plane

    auto input0 = model_with_preproc->get_parameters().at(0);
    auto input1 = model_with_preproc->get_parameters().at(1);
    ov::intel_gpu::ocl::ClImage2DTensor y_tensor = get_y_tensor();
    ov::intel_gpu::ocl::ClImage2DTensor uv_tensor = get_uv_tensor();
    infer_request.set_tensor(input0->get_friendly_name(), y_tensor);
    infer_request.set_tensor(input1->get_friendly_name(), uv_tensor);
    infer_request.infer();

single-plane

    auto input_yuv = model_with_preproc->input(0);
    ov::intel_gpu::ocl::ClImage2DTensor yuv_tensor = get_yuv_tensor();
    infer_request.set_tensor(input_yuv.get_any_name(), yuv_tensor);
    infer_request.infer();

NV12 to Grey

    cl::Image2D img_y_plane;
    auto input_y = model_with_preproc->input(0);
    auto remote_y_tensor = remote_context.create_tensor(input_y.get_element_type(), input.get_shape(), img_y_plane);
    infer_request.set_tensor(input_y.get_any_name(), remote_y_tensor);
    infer_request.infer();

Multiple Batches

two-plane

    auto input0 = model_with_preproc->get_parameters().at(0);
    auto input1 = model_with_preproc->get_parameters().at(1);
    std::vector<ov::Tensor> y_tensors = {y_tensor_0, y_tensor_1};
    std::vector<ov::Tensor> uv_tensors = {uv_tensor_0, uv_tensor_1};
    infer_request.set_tensors(input0->get_friendly_name(), y_tensors);
    infer_request.set_tensors(input1->get_friendly_name(), uv_tensors);
    infer_request.infer();

single-plane

    auto input_yuv = model_with_preproc->input(0);
    std::vector<ov::Tensor> yuv_tensors = {yuv_tensor_0, yuv_tensor_1};
    infer_request.set_tensors(input_yuv.get_any_name(), yuv_tensors);
    infer_request.infer();

NV12 to Grey

    cl::Image2D img_y_plane_0, img_y_plane_l;
    auto input_y = model_with_preproc->input(0);
    auto remote_y_tensor_0 = remote_context.create_tensor(input_y.get_element_type(), input.get_shape(), img_y_plane_0);
    auto remote_y_tensor_1 = remote_context.create_tensor(input_y.get_element_type(), input.get_shape(), img_y_plane_l);
    std::vector<ov::Tensor> y_tensors = {remote_y_tensor_0, remote_y_tensor_1};
    infer_request.set_tensors(input_y.get_any_name(), y_tensors);
    infer_request.infer();

I420 color format can be processed in a similar way

Context & Queue Sharing

The GPU plugin supports creation of shared context from the cl_command_queue handle. In that case, the opencl context handle is extracted from the given queue via OpenCL™ API, and the queue itself is used inside the plugin for further execution of inference primitives. Sharing the queue changes the behavior of the ov::InferRequest::start_async() method to guarantee that submission of inference primitives into the given queue is finished before returning control back to the calling thread.

This sharing mechanism allows performing pipeline synchronization on the app side and avoiding blocking the host thread on waiting for the completion of inference. The pseudo-code may look as follows:

Queue and context sharing example

    // ...

    // initialize the core and read the model
    ov::Core core;
    auto model = core.read_model("model.xml");

    // get opencl queue object
    cl::CommandQueue queue = get_ocl_queue();
    cl::Context cl_context = get_ocl_context();

    // share the queue with GPU plugin and compile model
    auto remote_context = ov::intel_gpu::ocl::ClContext(core, queue.get());
    auto exec_net_shared = core.compile_model(model, remote_context);

    auto input = model->get_parameters().at(0);
    auto input_size = ov::shape_size(input->get_shape());
    auto output = model->get_results().at(0);
    auto output_size = ov::shape_size(output->get_shape());
    cl_int err;

    // create the OpenCL buffers within the context
    cl::Buffer shared_in_buffer(cl_context, CL_MEM_READ_WRITE, input_size, NULL, &err);
    cl::Buffer shared_out_buffer(cl_context, CL_MEM_READ_WRITE, output_size, NULL, &err);
    // wrap in and out buffers into RemoteTensor and set them to infer request
    auto shared_in_blob = remote_context.create_tensor(input->get_element_type(), input->get_shape(), shared_in_buffer);
    auto shared_out_blob = remote_context.create_tensor(output->get_element_type(), output->get_shape(), shared_out_buffer);
    auto infer_request = exec_net_shared.create_infer_request();
    infer_request.set_tensor(input, shared_in_blob);
    infer_request.set_tensor(output, shared_out_blob);

    // ...
    // execute user kernel
    cl::Program program;
    cl::Kernel kernel_preproc(program, "user_kernel_preproc");
    kernel_preproc.setArg(0, shared_in_buffer);
    queue.enqueueNDRangeKernel(kernel_preproc,
                               cl::NDRange(0),
                               cl::NDRange(input_size),
                               cl::NDRange(1),
                               nullptr,
                               nullptr);
    // Blocking clFinish() call is not required, but this barrier is added to the queue to guarantee that user kernel is finished
    // before any inference primitive is started
    queue.enqueueBarrierWithWaitList(nullptr, nullptr);
    // ...

    // pass results to the inference
    // since the remote context is created with queue sharing, start_async() guarantees that scheduling is finished
    infer_request.start_async();

    // execute some postprocessing kernel.
    // infer_request.wait() is not called, synchonization between inference and post-processing is done via
    // enqueueBarrierWithWaitList call.
    cl::Kernel kernel_postproc(program, "user_kernel_postproc");
    kernel_postproc.setArg(0, shared_out_buffer);
    queue.enqueueBarrierWithWaitList(nullptr, nullptr);
    queue.enqueueNDRangeKernel(kernel_postproc,
                               cl::NDRange(0),
                               cl::NDRange(output_size),
                               cl::NDRange(1),
                               nullptr,
                               nullptr);

    // Wait for pipeline completion
    queue.finish();

Limitations

• Some primitives in the GPU plugin may block the host thread on waiting for the previous primitives before adding its kernels to the command queue. In such cases, the ov::InferRequest::start_async() call takes much more time to return control to the calling thread as internally it waits for a partial or full network completion. Examples of operations: Loop, TensorIterator, DetectionOutput, NonMaxSuppression

• Synchronization of pre/post processing jobs and inference pipeline inside a shared queue is user’s responsibility.

• Throughput mode is not available when queue sharing is used, i.e., only a single stream can be used for each compiled model.

Low-Level Methods for RemoteContext and RemoteTensor Creation

The high-level wrappers mentioned above bring a direct dependency on native APIs to the user program. If you want to avoid the dependency, you still can directly use the ov::Core::create_context(), ov::RemoteContext::create_tensor(), and ov::RemoteContext::get_params() methods. On this level, native handles are re-interpreted as void pointers and all arguments are passed using ov::AnyMap containers that are filled with std::string, ov::Any pairs. Two types of map entries are possible: descriptor and container. Descriptor sets the expected structure and possible parameter values of the map.

For possible low-level properties and their description, refer to the openvino/runtime/intel_gpu/remote_properties.hpp header file .

Examples

To see pseudo-code of usage examples, refer to the sections below.

Note

For low-level parameter usage examples, see the source code of user-side wrappers from the include files mentioned above.

OpenCL Kernel Execution on a Shared Buffer

This example uses the OpenCL context obtained from a compiled model object.

    // ...

    // initialize the core and load the network
    ov::Core core;
    auto model = core.read_model("model.xml");
    auto compiled_model = core.compile_model(model, "GPU");
    auto infer_request = compiled_model.create_infer_request();


    // obtain the RemoteContext from the compiled model object and cast it to ClContext
    auto gpu_context = compiled_model.get_context().as<ov::intel_gpu::ocl::ClContext>();
    // obtain the OpenCL context handle from the RemoteContext,
    // get device info and create a queue
    cl::Context cl_context = gpu_context;
    cl::Device device = cl::Device(cl_context.getInfo<CL_CONTEXT_DEVICES>()[0].get(), true);
    cl_command_queue_properties props = CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE;
    cl::CommandQueue queue = cl::CommandQueue(cl_context, device, props);

    // create the OpenCL buffer within the obtained context
    auto input = model->get_parameters().at(0);
    auto input_size = ov::shape_size(input->get_shape());
    cl_int err;
    cl::Buffer shared_buffer(cl_context, CL_MEM_READ_WRITE, input_size, NULL, &err);
    // wrap the buffer into RemoteBlob
    auto shared_blob = gpu_context.create_tensor(input->get_element_type(), input->get_shape(), shared_buffer);

    // ...
    // execute user kernel
    cl::Program program;
    cl::Kernel kernel(program, "user_kernel");
    kernel.setArg(0, shared_buffer);
    queue.enqueueNDRangeKernel(kernel,
                               cl::NDRange(0),
                               cl::NDRange(input_size),
                               cl::NDRange(1),
                               nullptr,
                               nullptr);
    queue.finish();
    // ...
    // pass results to the inference
    infer_request.set_tensor(input, shared_blob);
    infer_request.infer();

Running GPU Plugin Inference within User-Supplied Shared Context

    cl::Context ctx = get_ocl_context();

    ov::Core core;
    auto model = core.read_model("model.xml");

    // share the context with GPU plugin and compile ExecutableNetwork
    auto remote_context = ov::intel_gpu::ocl::ClContext(core, ctx.get());
    auto exec_net_shared = core.compile_model(model, remote_context);
    auto inf_req_shared = exec_net_shared.create_infer_request();


    // ...
    // do OpenCL processing stuff
    // ...

    // run the inference
    inf_req_shared.infer();

Direct Consuming of the NV12 VAAPI Video Decoder Surface on Linux

    // ...

    using namespace ov::preprocess;
    auto p = PrePostProcessor(model);
    p.input().tensor().set_element_type(ov::element::u8)
                      .set_color_format(ov::preprocess::ColorFormat::NV12_TWO_PLANES, {"y", "uv"})
                      .set_memory_type(ov::intel_gpu::memory_type::surface);
    p.input().preprocess().convert_color(ov::preprocess::ColorFormat::BGR);
    p.input().model().set_layout("NCHW");
    model = p.build();

    VADisplay disp = get_va_display();
    // create the shared context object
    auto shared_va_context = ov::intel_gpu::ocl::VAContext(core, disp);
    // compile model within a shared context
    auto compiled_model = core.compile_model(model, shared_va_context);

    auto input0 = model->get_parameters().at(0);
    auto input1 = model->get_parameters().at(1);

    auto shape = input0->get_shape();
    auto width = shape[1];
    auto height = shape[2];

    // execute decoding and obtain decoded surface handle
    VASurfaceID va_surface = decode_va_surface();
    //     ...
    //wrap decoder output into RemoteBlobs and set it as inference input
    auto nv12_blob = shared_va_context.create_tensor_nv12(height, width, va_surface);

    auto infer_request = compiled_model.create_infer_request();
    infer_request.set_tensor(input0->get_friendly_name(), nv12_blob.first);
    infer_request.set_tensor(input1->get_friendly_name(), nv12_blob.second);
    infer_request.start_async();
    infer_request.wait();

See Also

• ov::Core

• ov::RemoteTensor