Transition from Intel MKL-DNN to oneDNN {#dev_guide_transition_to_dnnl}
=======================================================================

To simplify library naming and differentiate it from Intel MKL, starting with
version 1.1 the library name is changed to
**Deep Neural Network Library (DNNL)**.

@note Subsequent library name change to
oneAPI Deep Neural Network Library (oneDNN) does not impact API, environment
variables, or build options.

The library will maintain backward compatibility with respect to API,
environment variables and build options until the next major release.
However, there are some incompatibilities that are described in
[Broken compatibility with Intel MKL-DNN](@ref dg_mtdt_s2) section below.

## 1. Summary of Changes

In short, the migration can be as simple as just replacing all
`MKLDNN/mkldnn` substrings with `DNNL/dnnl`.

### 1.1. Source code changes

All headers, functions, types, and namespaces renamed by replacing `mkldnn`
with `dnnl`. The macros with `MKLDNN` are replaced with `DNNL` counterparts.

An example of code with Intel MKL-DNN v1.0:
~~~ cpp
#include "mkldnn.hpp"

using namespace mkldnn;

mkldnn_memory_desc_t md;
if (md.format_kind == mkldnn_blocked) {}
conv.exec(stream, {{MKLDNN_ARGS_SRC, src}, ...});
~~~

The updated example with DNNL v1.1:
~~~ cpp
#include "dnnl.hpp"

using namespace dnnl;

dnnl_memory_desc_t md;
if (md.format_kind == dnnl_blocked) {}
conv.exec(stream, {{DNNL_ARGS_SRC, src}, ...});
~~~

To API compatibility with Intel MKL-DNN is based on
``include/mkldnn_dnnl_mangling.h`` header file that maps all Intel MKL-DNN
symbols to DNNL ones using C preprocessor:

~~~ cpp
// ...
#define mkldnn_memory_desc_t           dnnl_memory_desc_t
#define mkldnn_memory_desc_init_by_tag dnnl_memory_desc_init_by_tag
// ...
~~~

This file is included to every former Intel MKL-DNN header files
(for instance, see `mkldnn.h`) along with the DNNL counterpart.

### 1.2. Build process

The changes to the build options are similar to the ones in the source code.
All the options and namespace with `MKLDNN` are replaced with `DNNL`:

| Intel MKL-DNN                 | DNNL                        |
| :--                           | :--                         |
| MKLDNN (namespace)            | DNNL (namespace)            |
| MKLDNN_ARCH_OPT_FLAGS         | DNNL_ARCH_OPT_FLAGS         |
| MKLDNN_BUILD_EXAMPLES         | DNNL_BUILD_EXAMPLES         |
| MKLDNN_BUILD_FOR_CI           | DNNL_BUILD_FOR_CI           |
| MKLDNN_BUILD_TESTS            | DNNL_BUILD_TESTS            |
| MKLDNN_CPU_RUNTIME            | DNNL_CPU_RUNTIME            |
| MKLDNN_ENABLE_CONCURRENT_EXEC | DNNL_ENABLE_CONCURRENT_EXEC |
| MKLDNN_ENABLE_JIT_PROFILING   | DNNL_ENABLE_JIT_PROFILING   |
| MKLDNN_GPU_BACKEND            | DNNL_GPU_BACKEND            |
| MKLDNN_GPU_RUNTIME            | DNNL_GPU_RUNTIME            |
| MKLDNN_INSTALL_MODE           | DNNL_INSTALL_MODE           |
| MKLDNN_LIBRARY_TYPE           | DNNL_LIBRARY_TYPE           |
| MKLDNN_THREADING              | DNNL_THREADING              |
| MKLDNN_USE_CLANG_SANITIZER    | DNNL_USE_CLANG_SANITIZER    |
| MKLDNN_VERBOSE                | DNNL_VERBOSE                |
| MKLDNN_WERROR                 | DNNL_WERROR                 |

Similarly to the source code, DNNL preserves compatibility for build process
as well. It should be possible to continue using:

~~~ sh
# Through find package
find_package(mkldnn MKLDNN CONFIG REQUIRED)
target_link_libraries(project_app MKLDNN::mkldnn)

# Or direct sub-project inclusion
add_subdirectory(${MKLDNN_DIR} MKLDNN)
include_directories(${MKLDNN_DIR}/include)
target_link_libraries(project_app mkldnn)
~~~

Though it is preferable to switch to:

~~~ sh
# Through find package
find_package(dnnl DNNL CONFIG REQUIRED)
target_link_libraries(project_app DNNL::dnnl)

# Or direct sub-project inclusion
add_subdirectory(${DNNL_DIR} DNNL)
include_directories(${DNNL_DIR}/include)
target_link_libraries(project_app dnnl)
~~~

In case both style options are set, the DNNL one takes precedence.

### 1.3. Runtime parameters

DNNL supports both old Intel MKL-DNN and new DNNL environment variable controls.
DNNL value takes precedence over Intel MKL-DNN ones.

|                                                      | Intel MKL-DNN   | DNNL          |
| :--                                                  | :--             | :--           |
| [Verbose](@ref dev_guide_verbose)                    | MKLDNN_VERBOSE  | DNNL_VERBOSE  |
| [Dumping jit kernels](@ref dev_guide_inspecting_jit) | MKLDNN_JIT_DUMP | DNNL_JIT_DUMP |

@anchor dg_mtdt_s2
## 2. Broken compatibility with Intel MKL-DNN

Unfortunately, the full compatibility after renaming is not implemented.
DNNL is **not** compatible with Intel MKL-DNN in the following things:
- ABI: An application or a library built with Intel MKL-DNN cannot switch on
  using DNNL without re-compilation.
- Microsoft\* Visual Studio Solution files that are generated by cmake will
  be based on DNNL name only
  (e.g. `MKLDNN.sln` becomes `DNNL.sln`, and the former is no more generated).

## 3. Information for developers

The implementation of renaming (several patches and scripts that rename the
library) can be found
[here](https://github.com/emfomenk/intel-mkldnn-rebranding).

Also it is worth mentioning that DNNL team finally switched to the mandatory
code formatting based on `_clang-format` file in the root of the repository.
The corresponding changes were done by
[this](https://github.com/oneapi-src/oneDNN/commit/56ef626d6627e93da039c15e032603e1a4bc8af4)
and neighbor commits.
