Fix oneDNN feature name in MxNET

cd/utils/artifact_repository.py

@@ -291,7 +291,7 @@ def probe_cpu_variant(mxnet_features: Dict[str, bool]) -> str:
     :return: Either cpu, or mkl as the variant
     """
     logger.debug('Determining cpu variant')
-    if not mxnet_features['MKLDNN']:
+    if not mxnet_features['ONEDNN']:
         logger.debug('variant is: native')
         return 'native'
 
@@ -312,7 +312,7 @@ def probe_gpu_variant(mxnet_features: Dict[str, bool]) -> Optional[str]:
     cuda_version = get_cuda_version()
     if cuda_version:
         variant = 'cu{}'.format(cuda_version)
-        if not mxnet_features['MKLDNN']:
+        if not mxnet_features['ONEDNN']:
             RuntimeError('Error determining mxnet variant: ONEDNN should be enabled for cuda variants')
         logger.debug('variant is: {}'.format(variant))
         return variant

cd/utils/test_artifact_repository.py

@@ -161,26 +161,26 @@ def test_get_cuda_version_not_found(self, mock):
     @patch('artifact_repository.get_libmxnet_features')
     def test_probe_variant_native(self, mock_features):
         """
-        Tests 'native' is returned if MKLDNN and CUDA features are OFF
+        Tests 'native' is returned if ONEDNN and CUDA features are OFF
         """
-        mock_features.return_value = {'MKLDNN': False, 'CUDA': False}
+        mock_features.return_value = {'ONEDNN': False, 'CUDA': False}
         self.assertEqual(probe_mxnet_variant('libmxnet.so'), 'native')
 
     @patch('artifact_repository.get_libmxnet_features')
     def test_probe_variant_cpu(self, mock_features):
         """
-        Tests 'cpu' is returned if MKLDNN is ON and CUDA is OFF
+        Tests 'cpu' is returned if ONEDNN is ON and CUDA is OFF
         """
-        mock_features.return_value = {'MKLDNN': True, 'CUDA': False}
+        mock_features.return_value = {'ONEDNN': True, 'CUDA': False}
         self.assertEqual(probe_mxnet_variant('libmxnet.so'), 'cpu')
 
     @patch('artifact_repository.get_libmxnet_features')
     @patch('artifact_repository.get_cuda_version')
     def test_probe_variant_cuda(self, mock_cuda_version, mock_features):
         """
-        Tests 'cu102' is returned if MKLDNN is OFF and CUDA is ON and CUDA version is 10.2
+        Tests 'cu102' is returned if ONEDNN is OFF and CUDA is ON and CUDA version is 10.2
         """
-        mock_features.return_value = {'MKLDNN': True, 'CUDA': True}
+        mock_features.return_value = {'ONEDNN': True, 'CUDA': True}
         mock_cuda_version.return_value = '102'
         self.assertEqual(probe_mxnet_variant('libmxnet.so'), 'cu102')
 
@@ -198,7 +198,7 @@ def test_probe_variant_cuda_mkl(self, mock_cuda_version, mock_features):
         """
         Tests exception is raised if CUDA feature is ON but cuda version could not be determined
         """
-        mock_features.return_value = {'MKLDNN': True, 'CUDA': True}
+        mock_features.return_value = {'ONEDNN': True, 'CUDA': True}
         mock_cuda_version.return_value = None
         with self.assertRaises(RuntimeError):
             probe_mxnet_variant('libmxnet.so')

include/mxnet/libinfo.h

@@ -172,8 +172,8 @@ enum : unsigned {
   // Other math libraries:
   // Linear Algebra PACKage
   LAPACK,
-  // Intel(R) Math Kernel Library for Deep Neural Networks
-  MKLDNN,
+  // oneAPI Deep Neural Network Library (oneDNN)
+  ONEDNN,
 
   // Image processing
   OPENCV,

python/mxnet/amp/lists/symbol_fp16.py

@@ -600,7 +600,7 @@
     '_contrib_sldwin_atten_context',
     ]
 
-if Features().is_enabled('MKLDNN'):
+if Features().is_enabled('ONEDNN'):
     FP32_FUNCS.extend([
         '_sg_mkldnn_conv',
         '_sg_mkldnn_fully_connected',

python/mxnet/runtime.py

@@ -40,7 +40,7 @@
     [✖ CUDA, ✖ CUDNN, ✖ NCCL, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3,
     ✔ CPU_SSE4_1, ✔ CPU_SSE4_2, ✖ CPU_SSE4A, ✔ CPU_AVX, ✖ CPU_AVX2, ✔ OPENMP, ✖ SSE,
     ✔ F16C, ✔ JEMALLOC, ✔ BLAS_OPEN, ✖ BLAS_ATLAS, ✖ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK,
-    ✖ MKLDNN, ✔ OPENCV, ✖ DIST_KVSTORE, ✖ INT64_TENSOR_SIZE, ✔ SIGNAL_HANDLER, ✔ DEBUG, ✖ TVM_OP]
+    ✖ ONEDNN, ✔ OPENCV, ✖ DIST_KVSTORE, ✖ INT64_TENSOR_SIZE, ✔ SIGNAL_HANDLER, ✔ DEBUG, ✖ TVM_OP]
 
 
 """

src/libinfo.cc

@@ -78,7 +78,7 @@ class FeatureSet {
     feature_bits.set(BLAS_MKL, MXNET_USE_BLAS_MKL);
     feature_bits.set(BLAS_APPLE, MXNET_USE_BLAS_APPLE);
     feature_bits.set(LAPACK, MXNET_USE_LAPACK);
-    feature_bits.set(MKLDNN, MXNET_USE_ONEDNN);
+    feature_bits.set(ONEDNN, MXNET_USE_ONEDNN);
 
     // Image
     feature_bits.set(OPENCV, MXNET_USE_OPENCV);
@@ -152,7 +152,7 @@ const std::vector<std::string> EnumNames::names = {
   "BLAS_MKL",
   "BLAS_APPLE",
   "LAPACK",
-  "MKLDNN",
+  "ONEDNN",
   "OPENCV",
   "DIST_KVSTORE",
   "INT64_TENSOR_SIZE",

tools/dependencies/README.md

@@ -49,12 +49,12 @@ MXNet is built on top of many dependencies. Managing these dependencies could be
 
 ## Overview
 
-The dependencies could be categorized by several groups: BLAS libraries, CPU-based performance boost library, i.e. MKLDNN and GPU-based performance boosting library including CUDA, cuDNN, NCCL. and others including OpenCV, Numpy, S3-related, PS-lite dependencies. The list below shows all the dependencies and their version. Except for CUDA, cuDNN, NCCL which the user is required to install on their environments, we statically link those dependencies into libmxnet.so when we build PyPi package. By doing this, the user can take advantage of these dependencies without being worry about it.
+The dependencies could be categorized by several groups: BLAS libraries, CPU-based performance boost library, i.e. ONEDNN and GPU-based performance boosting library including CUDA, cuDNN, NCCL. and others including OpenCV, Numpy, S3-related, PS-lite dependencies. The list below shows all the dependencies and their version. Except for CUDA, cuDNN, NCCL which the user is required to install on their environments, we statically link those dependencies into libmxnet.so when we build PyPi package. By doing this, the user can take advantage of these dependencies without being worry about it.
 
 | Dependencies  | MXNet Version |
 | :------------: |:-------------:| 
 |OpenBLAS| 0.3.9 |
-|MKLDNN| 0.19 | 
+|ONEDNN| 2.0 | 
 |CUDA| 10.1 |
 |cuDNN| 7.5.1 |
 |NCCL| 2.4.2 |
@@ -102,7 +102,7 @@ sudo apt-get install -y git \
     pkg-config
 ```
 
-### MKL, MKLDNN
+### MKL, ONEDNN
 
 @pengzhao-intel (https://github.com/apache/incubator-mxnet/commits?author=pengzhao-intel) and his team are tracking and updating these versions. Kudos to them!
 

tools/pip/setup.py

@@ -141,14 +141,14 @@ def skip_markdown_comments(md):
         libraries.append('CUDA-10.1')
 
 from mxnet.runtime import Features
-if Features().is_enabled("MKLDNN"):
-    libraries.append('MKLDNN')
+if Features().is_enabled("ONEDNN"):
+    libraries.append('ONEDNN')
 
 short_description += ' This version uses {0}.'.format(' and '.join(libraries))
 
 package_data = {'mxnet': [os.path.join('mxnet', os.path.basename(LIB_PATH[0]))],
                 'dmlc_tracker': []}
-if Features().is_enabled("MKLDNN"):
+if Features().is_enabled("ONEDNN"):
     shutil.copytree(os.path.join(CURRENT_DIR, 'mxnet-build/3rdparty/onednn/include'),
                     os.path.join(CURRENT_DIR, 'mxnet/include/onednn'))
 if platform.system() == 'Linux':