Build Options¶
oneDNN supports the following build-time options.
CMake Option |
Supported values (defaults in bold) |
Desc |
|---|---|---|
ONEDNN_LIBRARY_TYPE |
SHARED , STATIC |
Defines the resulting library type |
ONEDNN_CPU_RUNTIME |
NONE, OMP , TBB, SEQ, THREADPOOL, DPCPP |
Defines the threading runtime for CPU engines |
ONEDNN_GPU_RUNTIME |
NONE , OCL, DPCPP |
Defines the offload runtime for GPU engines |
ONEDNN_BUILD_EXAMPLES |
ON , OFF |
Controls building the examples |
ONEDNN_BUILD_TESTS |
ON , OFF |
Controls building the tests |
ONEDNN_ARCH_OPT_FLAGS |
compiler flags |
Specifies compiler optimization flags (see warning note below) |
ONEDNN_ENABLE_CONCURRENT_EXEC |
ON, OFF |
Disables sharing a common scratchpad between primitives in dnnl::scratchpad_mode::library mode |
ONEDNN_ENABLE_JIT_PROFILING |
ON , OFF |
|
ONEDNN_ENABLE_PRIMITIVE_CACHE |
ON , OFF |
Enables primitive cache |
ONEDNN_ENABLE_MAX_CPU_ISA |
ON , OFF |
Enables CPU dispatcher controls |
ONEDNN_ENABLE_CPU_ISA_HINTS |
ON , OFF |
Enables CPU ISA hints |
ONEDNN_ENABLE_WORKLOAD |
TRAINING , INFERENCE |
Specifies a set of functionality to be available based on workload |
ONEDNN_ENABLE_PRIMITIVE |
ALL , PRIMITIVE_NAME |
Specifies a set of functionality to be available based on primitives |
ONEDNN_ENABLE_PRIMITIVE_CPU_ISA |
ALL , ISA_NAME |
Specifies a set of functionality to be available based on ISA |
ONEDNN_VERBOSE |
ON , OFF |
Enables verbose mode |
ONEDNN_AARCH64_USE_ACL |
ON, OFF |
Enables integration with Arm Compute Library for AArch64 builds |
ONEDNN_BLAS_VENDOR |
NONE , ARMPL |
Defines an external BLAS library to link to for GEMM-like operations |
ONEDNN_GPU_VENDOR |
INTEL , NVIDIA |
Defines GPU vendor for GPU engines |
ONEDNN_DPCPP_HOST_COMPILER |
DEFAULT , GNU C++ compiler executable |
Specifies host compiler executable for DPCPP runtimes |
ONEDNN_LIBRARY_NAME |
dnnl , library name |
Specifies name of the library |
All building options listed support their counterparts with DNNL prefix instead of ONEDNN. DNNL options would take precedence over ONEDNN versions, if both versions are specified.
All other building options or values that can be found in CMake files are intended for development/debug purposes and are subject to change without notice. Please avoid using them.
Common options¶
Host compiler¶
When building oneDNN with oneAPI DPC++/C++ Compiler user can specify a custom host compiler. The host compiler is a compiler that will be used by the main compiler driver to perform host compilation step.
The host compiler can be specified with ONEDNN_DPCPP_HOST_COMPILER CMake option. It should be specified either by name (in this case, the standard system environment variables will be used to discover it) or an absolute path to the compiler executable.
The default value of ONEDNN_DPCPP_HOST_COMPILER is DEFAULT, which is the default host compiler used by the compiler specified with CMAKE_CXX_COMPILER.
The DEFAULT host compiler is the only supported option on Windows. On Linux, user can specify a GNU C++ compiler as the host compiler.
Warning
oneAPI DPC++/C++ Compiler requires host compiler to be compatible. The minimum allowed GNU C++ compiler version is 7.4.0. See GCC* Compatibility and Interoperability section in oneAPI DPC++/C++ Compiler Developer Guide.
Configuring functionality¶
Using ONEDNN_ENABLE_WORKLOAD and ONEDNN_ENABLE_PRIMITIVE it is possible to limit functionality available in the final shared object or statically linked application. This helps to reduce the amount of disk space occupied by an app.
ONEDNN_ENABLE_WORKLOAD¶
This option supports only two values: TRAINING (the default) and INFERENCE. INFERENCE enables only forward propagation kind part of functionality, removing all backward-related functionality, except those which are dependencies for forward propagation kind part.
ONEDNN_ENABLE_PRIMITIVE¶
This option supports several values: ALL (the default) which enables all primitives implementations or a set of BATCH_NORMALIZATION, BINARY, CONCAT, CONVOLUTION, DECONVOLUTION, ELTWISE, INNER_PRODUCT, LAYER_NORMALIZATION, LRN, MATMUL, POOLING, PRELU, REDUCTION, REORDER, RESAMPLING, RNN, SHUFFLE, SOFTMAX, SUM. When a set is used, only those selected primitives implementations will be available. Attempting to use other primitive implementations will end up returning an unimplemented status when creating primitive descriptor. In order to specify a set, a CMake-style string should be used, with semicolon delimiters, as in this example:
-DONEDNN_ENABLE_PRIMITIVE=CONVOLUTION;MATMUL;REORDER
ONEDNN_ENABLE_PRIMITIVE_CPU_ISA¶
This option supports several values: ALL (the default) which enables all ISA implementations or one of SSE41, AVX2, AVX512, and AMX. Values are linearly ordered as SSE41 <AVX2 <AVX512 <AMX. When specified, selected ISA and all ISA that are “smaller” will be available. Example that enables SSE41 and AVX2 sets:
-DONEDNN_ENABLE_PRIMITIVE_CPU_ISA=AVX2
CPU Options¶
Intel Architecture Processors and compatible devices are supported by oneDNN CPU engine. The CPU engine is built by default but can be disabled at build time by setting ONEDNN_CPU_RUNTIME to NONE. In this case, GPU engine must be enabled.
Targeting Specific Architecture¶
oneDNN uses JIT code generation to implement most of its functionality and will choose the best code based on detected processor features. However, some oneDNN functionality will still benefit from targeting a specific processor architecture at build time. You can use ONEDNN_ARCH_OPT_FLAGS CMake option for this.
For Intel(R) C++ Compilers, the default option is -xSSE4.1, which instructs the compiler to generate the code for the processors that support SSE4.1 instructions. This option would not allow you to run the library on older processor architectures.
For GNU* Compilers and Clang, the default option is -msse4.1.
Warning
While use of ONEDNN_ARCH_OPT_FLAGS option gives better performance, the resulting library can be run only on systems that have instruction set compatible with the target instruction set. Therefore, ARCH_OPT_FLAGS should be set to an empty string ("") if the resulting library needs to be portable.
Runtime CPU dispatcher control¶
oneDNN JIT relies on ISA features obtained from the processor it is being run on. There are situations when it is necessary to control this behavior at run-time to, for example, test SSE4.1 code on an AVX2-capable processor. The ONEDNN_ENABLE_MAX_CPU_ISA build option controls the availability of this feature. See CPU Dispatcher Control for more information.
Runtime CPU ISA hints¶
For performance reasons, sometimes oneDNN JIT needs to be provided with extra hints so as to prefer or avoid particular CPU ISA feature. For example, one might want to disable Zmm registers usage in order to take advantage of higher clock speed. The ONEDNN_ENABLE_CPU_ISA_HINTS build option makes this feature available at runtime. See CPU ISA Hints for more information.
Runtimes¶
CPU engine can use OpenMP, Threading Building Blocks (TBB) or sequential threading runtimes. OpenMP threading is the default build mode. This behavior is controlled by the ONEDNN_CPU_RUNTIME CMake option.
OpenMP¶
oneDNN uses OpenMP runtime library provided by the compiler.
When building oneDNN with oneAPI DPC++/C++ Compiler the library will link to Intel OpenMP runtime. This behavior can be changed by changing the host compiler with ONEDNN_DPCPP_HOST_COMPILER option.
Warning
Because different OpenMP runtimes may not be binary-compatible, it’s important to ensure that only one OpenMP runtime is used throughout the application. Having more than one OpenMP runtime linked to an executable may lead to undefined behavior including incorrect results or crashes. However as long as both the library and the application use the same or compatible compilers there would be no conflicts.
Threading Building Blocks (TBB)¶
To build oneDNN with TBB support, set ONEDNN_CPU_RUNTIME to TBB :
$ cmake -DONEDNN_CPU_RUNTIME=TBB ..
Optionally, set the TBBROOT environmental variable to point to the TBB installation path or pass the path directly to CMake:
$ cmake -DONEDNN_CPU_RUNTIME=TBB -DTBBROOT=/opt/intel/path/tbb ..
oneDNN has functional limitations if built with TBB:
Winograd convolution algorithm is not supported for fp32 backward by data and backward by weights propagation.
Threadpool¶
To build oneDNN with support for threadpool threading, set ONEDNN_CPU_RUNTIME to THREADPOOL
$ cmake -DONEDNN_CPU_RUNTIME=THREADPOOL ..
The _ONEDNN_TEST_THREADPOOL_IMPL CMake variable controls which of the three threadpool implementations would be used for testing: STANDALONE, TBB, or EIGEN. The latter two require also passing TBBROOT or Eigen3_DIR paths to CMake. For example:
$ cmake -DONEDNN_CPU_RUNTIME=THREADPOOL -D_ONEDNN_TEST_THREADPOOL_IMPL=EIGEN -DEigen3_DIR=/path/to/eigen/share/eigen3/cmake ..
Threadpool threading support is experimental and has the same limitations as TBB plus more:
As threadpools are attached to streams which are only passed during primitive execution, work decomposition is performed statically at the primitive creation time. At the primitive execution time, the threadpool is responsible for balancing the static decomposition from the previous item across available worker threads.
AArch64 Options¶
oneDNN includes experimental support for Arm 64-bit Architecture (AArch64). By default, AArch64 builds will use the reference implementations throughout. The following options enable the use of AArch64 optimised implementations for a limited number of operations, provided by AArch64 libraries.
AArch64 build configuration |
CMake Option |
Environment variables |
Depe |
|---|---|---|---|
Arm Compute Library based primitives |
ONEDNN_AARCH64_USE_ACL=ON |
ACL_ROOT_DIR=*</path/to/ComputeLibrary>* |
|
Vendor BLAS library support |
ONEDNN_BLAS_VENDOR=ARMPL |
None |
Arm Compute Library¶
Arm Compute Library is an open-source library for machine learning applications. The development repository is available from mlplatform.org, and releases are also available on GitHub. The ONEDNN_AARCH64_USE_ACL CMake option is used to enable Compute Library integration:
$ cmake -DONEDNN_AARCH64_USE_ACL=ON ..
This assumes that the environment variable ACL_ROOT_DIR is set to the location of Arm Compute Library, which must be downloaded and built independently of oneDNN.
Warning
For a debug build of oneDNN it is advisable to specify a Compute Library build which has also been built with debug enabled.
Warning
oneDNN is only compatible with Compute Library builds v21.11 or later.
Vendor BLAS libraries¶
oneDNN can use a standard BLAS library for GEMM operations. The ONEDNN_BLAS_VENDOR build option controls BLAS library selection, and defaults to NONE. For AArch64 builds with GCC, use the Arm Performance Libraries :
$ cmake -DONEDNN_BLAS_VENDOR=ARMPL ..
Additional options available for development/debug purposes. These options are subject to change without notice, see https://github.com/oneapi-src/oneDNN/blob/master/cmake/options.cmake for details.
GPU Options¶
Intel Processor Graphics is supported by oneDNN GPU engine. GPU engine is disabled in the default build configuration.
Runtimes¶
To enable GPU support you need to specify the GPU runtime by setting ONEDNN_GPU_RUNTIME CMake option. The default value is "NONE" which corresponds to no GPU support in the library.
OpenCL*¶
OpenCL runtime requires Intel(R) SDK for OpenCL* applications. You can explicitly specify the path to the SDK using -DOPENCLROOT CMake option.
$ cmake -DONEDNN_GPU_RUNTIME=OCL -DOPENCLROOT=/path/to/opencl/sdk ..