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Experimental features

To test aggressive performance optimizations that might affect accuracy or new API and functionality without an impact to regular users, oneDNN provides experimental features.

Build-time Controls

There are two kinds of experimental features:

  1. Features that can be enabled at runtime with an environment variable. To enable such experimental features, the library should be built with a CMake option ONEDNN_EXPERIMENTAL=ON. Each experimental feature has to be individually selected using environment variables.

  2. Features that can be enabled only with a build time option. To enable such experimental features, the library should be built with a CMake option that corresponds to a particular feature.

Both kinds of experimental features can be enabled simultaneously.

Experimental features

Environment variable

Description

ONEDNN_EXPERIMENTAL_BNORM_STATS_ONE_PASS

Calculate mean and variance in batch normalization(BN) in single pass ( RFC ).

ONEDNN_EXPERIMENTAL_GPU_CONV_V2

Enable shapeless GPU convolution implementation (the feature is under development).

Build time option

Description

ONEDNN_EXPERIMENTAL_UKERNEL

Enable experimental microkernel APIs and functionalities.

ONEDNN_EXPERIMENTAL_PROFILING

Enable experimental profiling API.

ONEDNN_EXPERIMENTAL_LOGGING

Enable experimental logging support for oneDNN verbose mode.

Features details

ONEDNN_EXPERIMENTAL_UKERNEL

This option enables a new set of CPU-only APIs to support block-level functionalities. By composing these low-level, sequential operations, users can implement their own custom operations/fusions, and tailor blocking/threading logic to their applications.

More details on this API are available in the Microkernel APIs section”.

ONEDNN_EXPERIMENTAL_PROFILING

This option enables profiling API that can be used to query different profiling data.

There are two ways to use the profiling capabilities:

  • Create a queue with enabled profiling capabilities and use the interoperability API to create a oneDNN stream with the queue. The library will identify that the queue supports profiling and will collect profiling data

  • Create a oneDNN stream using runtime agnostic API and enable profiling capabilities using the stream flag stream::flags::profiling

Below is a pseudo-code that demonstrates the profiling API usage with a user-provided queue.

dnnl::engine engine(engine::kind::gpu, 0);
// Create a queue with enabled profiling mode.
cl_command_queue ocl_queue {};
cl_queue_properties props[] = {CL_QUEUE_PROPERTIES, CL_QUEUE_PROFILING_ENABLE, 0};
ocl_queue = clCreateCommandQueueWithProperties(ocl_interop::get_context(engine),
    ocl_interop::get_device(engine), props, ...);
// Create dnnl::stream with the queue.
dnnl::stream stream = ocl_interop::make_stream(engine, ocl_queue);
// Create a convolution primitive ... //
// Reset profiler's state.
dnnl::reset_profiling(stream);
// Enqueue same primitive twice and wait for both executions to complete.
conv_prim.execute(stream, ...)
conv_prim.execute(stream, ...)
stream.wait();
// Query profiling data. The vector size will be equal to the number of
// executions happened on the stream since the last `dnnl::reset_profiling`
// call.
std::vector<uint64_t> nsecs = dnnl::get_profiling_data(stream, profiling_data_kind::time);
assert(nsecs.size() == 2);
// Reset profiler's state.
dnnl::reset_profiling(stream);

Warning

  • When the stream is created with enabled profiling capabilities it will collect profiling data for each primitive execution. It is the user’s responsibility to reset the profiler’s state to avoid consuming all memory resources in the system.

Limitations

  • Only GPU engines with OpenCL and SYCL runtimes are supported

  • Only Intel vendor is supported for SYCL runtime

  • Out-of-order queue is not supported

Warning

  • Enabling some experimental features does not guarantee that the library will utilize them

  • Enabling some experimental features might change the accuracy of oneDNN primitives

ONEDNN_EXPERIMENTAL_LOGGING

This option introduces logging support in oneDNN which allows one to save the verbose outputs generated by oneDNN applications to user-specified logfiles. By setting ONEDNN_EXPERIMENTAL_LOGGING=ON, a logging mechanism is built into oneDNN using the third-party spdlog library. Logging can then be enabled while running different applications by specifying the logfile path using ONEDNN_VERBOSE_LOGFILE :

$ ONEDNN_VERBOSE=all ONEDNN_VERBOSE_LOGFILE=./logs/cnn_test_logger.log ./examples/cnn-inference-f32-cpp

When logging is enabled while running an application, it also requires that the verbose mode be enabled for the run using ONEDNN_VERBOSE. When no logfile is specified, logging is automatically disabled and the verbose output is printed only to the console. For the specified logfile path, the logger creates the base directory and the logfile if they do not already exist. When the specified logfile already exists, the output is appended to the existing file until it reaches the maximum file size. Note: Multiple instances using the same filepath for DNNL_VERBOSE_LOGFILE will write to the same file during the API run. The spdlog mechanism supports handling multiple instances concurrently if they write to the same logfile but the expectation is to specify different logfiles for different instances via the runtime variables.

By default, logging is disabled in oneDNN and any verbose output generated by oneDNN is printed only to stdout. The API is executed as a rotating lazy logger with a file size specified by ONEDNN_VERBOSE_LOGFILE_SIZE(=1024*1024*50). When logging is enabled, the user has the option to print verbose output to both stdout and the logfile by setting ONEDNN_VERBOSE_LOG_WITH_CONSOLE=1. The runtime controls for oneDNN logging are listed as follows:

Runtime variable

Description

ONEDNN_VERBOSE_LOGFILE

Enables verbose logging and specifies logfile path.

ONEDNN_VERBOSE_LOGFILE_SIZE

Specifies maximum size for the logfile.

ONEDNN_VERBOSE_NUM_LOGFILES

Number of rotating logfiles for the logger.

ONEDNN_VERBOSE_LOG_WITH_CONSOLE

Enables printing to both stdout and the logfile.
Sparse memory formats Ukernels