replace mkldnn to onednn in strings

test/auto_parallel/custom_op/utils.py

@@ -31,7 +31,7 @@ def get_paddle_includes():
     paddle_includes = []
     paddle_includes.append(f"{env_dict.get('PADDLE_SOURCE_DIR')}")
 
-    # mkldnn
+    # onednn
     if env_dict.get("WITH_ONEDNN") == 'ON':
         paddle_includes.append(f"{env_dict.get('ONEDNN_INSTALL_DIR')}/include")
     if env_dict.get("WITH_GPU") == 'ON' or env_dict.get("WITH_ROCM") == 'ON':

test/dygraph_to_static/test_mnist.py

@@ -173,18 +173,18 @@ def test_mnist_to_static(self):
         )
 
     @test_default_mode_only
-    def test_mnist_declarative_cpu_vs_mkldnn(self):
+    def test_mnist_declarative_cpu_vs_onednn(self):
         dygraph_loss_cpu = self.train_dygraph()
         paddle.set_flags({'FLAGS_use_onednn': True})
         try:
-            dygraph_loss_mkldnn = self.train_dygraph()
+            dygraph_loss_onednn = self.train_dygraph()
         finally:
             paddle.set_flags({'FLAGS_use_onednn': False})
         np.testing.assert_allclose(
             dygraph_loss_cpu,
-            dygraph_loss_mkldnn,
+            dygraph_loss_onednn,
             rtol=1e-05,
-            err_msg=f'cpu dygraph is {dygraph_loss_cpu}\n mkldnn dygraph is \n{dygraph_loss_mkldnn}',
+            err_msg=f'cpu dygraph is {dygraph_loss_cpu}\n onednn dygraph is \n{dygraph_loss_onednn}',
         )
 
     def train(self, to_static=False):

test/ir/inference/test_onednn_conv3d_op.py

@@ -22,7 +22,7 @@
 from program_config import OpConfig, ProgramConfig, TensorConfig
 
 
-class TestMkldnnConv3dOp(OnednnAutoScanTest):
+class TestOnednnConv3dOp(OnednnAutoScanTest):
     def is_program_valid(self, program_config: ProgramConfig) -> bool:
         return True
 

test/ir/inference/test_onednn_conv_affine_channel_fuse_pass.py

@@ -158,7 +158,7 @@ def teller2(program_config, predictor_config):
         self.add_ignore_check_case(
             teller2,
             IgnoreReasons.PASS_ACCURACY_ERROR,
-            "Currently mkldnn Output has diff with bias!",
+            "Currently onednn Output has diff with bias!",
         )
 
     def test(self):

test/ir/inference/test_onednn_matmulv2_op.py

@@ -22,7 +22,7 @@
 from program_config import OpConfig, ProgramConfig, TensorConfig
 
 
-class TestMkldnnMatmulv2Op(OnednnAutoScanTest):
+class TestOnednnMatmulv2Op(OnednnAutoScanTest):
     def is_program_valid(self, program_config: ProgramConfig) -> bool:
         if len(program_config.inputs["input_data2"].shape) == 4:
             if (

test/ir/inference/test_onednn_shuffle_channel_detect_pass.py

@@ -32,7 +32,7 @@ def product(input):
 
 
 @OpTestTool.skip_if_not_cpu()
-class TestShuffleChannelMKLDNNDetectPass(PassAutoScanTest):
+class TestShuffleChannelOneDNNDetectPass(PassAutoScanTest):
     def is_program_valid(self, program_config: ProgramConfig) -> bool:
         input_shape = program_config.inputs['input_data'].shape
         first_reshape2_shape = program_config.ops[0].attrs['shape']

test/legacy_test/test_fill_constant_op.py

@@ -542,7 +542,7 @@ def init_data(self):
         self.onednn_data_type = "bfloat16"
 
     def test_check_output(self):
-        # no dynamic graph test for mkldnn
+        # no dynamic graph test for onednn
         self.check_output_with_place(
             core.CPUPlace(), check_dygraph=False, check_pir=False
         )

test/onednn/test_elementwise_mul_bf16_onednn_op.py

@@ -24,7 +24,7 @@
 @unittest.skipIf(
     not core.supports_bfloat16(), "place does not support BF16 evaluation"
 )
-class TestElementwiseMulBf16MklDNNOp(OpTest):
+class TestElementwiseMulBf16OneDNNOp(OpTest):
     def setUp(self):
         self.op_type = "elementwise_mul"
         self.use_onednn = True
@@ -87,8 +87,8 @@ def test_check_grad_ignore_y(self):
         )
 
 
-class TestElementwiseMulBroadcastingBf16MklDNNOp(
-    TestElementwiseMulBf16MklDNNOp
+class TestElementwiseMulBroadcastingBf16OneDNNOp(
+    TestElementwiseMulBf16OneDNNOp
 ):
     def generate_data(self):
         self.x = np.random.uniform(1, 2, [1, 2, 3, 100]).astype(np.float32)

test/onednn/test_matmul_bf16_onednn_op.py

@@ -24,7 +24,7 @@
 @unittest.skipIf(
     not core.supports_bfloat16(), "place does not support BF16 evaluation"
 )
-class TestMatmulBf16MklDNNOp(OpTest):
+class TestMatmulBf16OneDNNOp(OpTest):
     def generate_data(self):
         self.x_fp32 = np.random.random((25, 2, 2)).astype(np.float32)
         self.y_fp32 = np.random.random((25, 2, 2)).astype(np.float32)
@@ -123,22 +123,22 @@ def calculate_grads(self):
         self.dout = dout
 
 
-class TestDnnlMatMulOpAlpha(TestMatmulBf16MklDNNOp):
+class TestDnnlMatMulOpAlpha(TestMatmulBf16OneDNNOp):
     def generate_data(self):
         self.x_fp32 = np.random.random((17, 2, 3)).astype(np.float32)
         self.y_fp32 = np.random.random((17, 3, 2)).astype(np.float32)
         self.alpha = 2.0
         self.out = self.alpha * np.matmul(self.x_fp32, self.y_fp32)
 
 
-class TestDnnlMatMulOp2D(TestMatmulBf16MklDNNOp):
+class TestDnnlMatMulOp2D(TestMatmulBf16OneDNNOp):
     def generate_data(self):
         self.x_fp32 = np.random.random((12, 9)).astype(np.float32)
         self.y_fp32 = np.random.random((9, 12)).astype(np.float32)
         self.out = np.matmul(self.x_fp32, self.y_fp32)
 
 
-class TestDnnlMatMulOpTransposeX(TestMatmulBf16MklDNNOp):
+class TestDnnlMatMulOpTransposeX(TestMatmulBf16OneDNNOp):
     def generate_data(self):
         self.x_fp32 = np.random.random((12, 9)).astype(np.float32)
         self.y_fp32 = np.random.random((12, 9)).astype(np.float32)
@@ -153,7 +153,7 @@ def set_attributes(self):
         }
 
 
-class TestDnnlMatMulOpTransposeY(TestMatmulBf16MklDNNOp):
+class TestDnnlMatMulOpTransposeY(TestMatmulBf16OneDNNOp):
     def generate_data(self):
         self.x_fp32 = np.random.random((12, 9)).astype(np.float32)
         self.y_fp32 = np.random.random((12, 9)).astype(np.float32)
@@ -168,7 +168,7 @@ def set_attributes(self):
         }
 
 
-class TestMatmulBf16MklDNNForceFp32Output(TestMatmulBf16MklDNNOp):
+class TestMatmulBf16OneDNNForceFp32Output(TestMatmulBf16OneDNNOp):
     def generate_data(self):
         self.x_fp32 = np.random.random((12, 9)).astype(np.float32)
         self.y_fp32 = np.random.random((9, 12)).astype(np.float32)

test/quantization/quant2_int8_lstm_model.py

@@ -52,7 +52,7 @@ def parse_args():
         '--onednn_cache_capacity',
         type=int,
         default=0,
-        help='Mkldnn cache capacity. The default value in Python API is 15, which can slow down int8 models. Default 0 means unlimited cache.',
+        help='Onednn cache capacity. The default value in Python API is 15, which can slow down int8 models. Default 0 means unlimited cache.',
     )
 
     test_args, args = parser.parse_known_args(namespace=unittest)