test/deprecated/ir/inference modify use_mkldnn [fluid_ops]

test/deprecated/ir/inference/inference_pass_test.py

@@ -37,7 +37,7 @@ def __init__(self, methodName='runTest'):
         self.feeds = None
         self.fetch_list = None
 
-        self.enable_mkldnn = False
+        self.enable_onednn = False
         self.enable_onednn_bfloat16 = False
         self.enable_trt = False
         self.enable_tensorrt_varseqlen = False
@@ -284,10 +284,10 @@ def check_output_with_option(
                 )
 
         # Check whether the onednn results and the CPU results are the same.
-        if (not use_gpu) and self.enable_mkldnn:
+        if (not use_gpu) and self.enable_onednn:
             onednn_outputs = self._get_inference_outs(
                 self._get_analysis_config(
-                    use_gpu=use_gpu, use_onednn=self.enable_mkldnn
+                    use_gpu=use_gpu, use_onednn=self.enable_onednn
                 )
             )
 

test/deprecated/ir/inference/quant_dequant_test.py

@@ -46,7 +46,7 @@ def __init__(self, methodName='runTest'):
         self.test_startup_program = paddle.static.Program()
         self.feeds = None
         self.fetch_list = None
-        self.enable_mkldnn = False
+        self.enable_onednn = False
         self.enable_onednn_bfloat16 = False
         self.enable_trt = False
         self.enable_tensorrt_varseqlen = True
@@ -387,10 +387,10 @@ def check_output_with_option(
                 )
 
         # Check whether the onednn results and the CPU results are the same.
-        if (not use_gpu) and self.enable_mkldnn:
+        if (not use_gpu) and self.enable_onednn:
             onednn_outputs = self._get_inference_outs(
                 self._get_analysis_config(
-                    use_gpu=use_gpu, use_onednn=self.enable_mkldnn
+                    use_gpu=use_gpu, use_onednn=self.enable_onednn
                 )
             )
 

test/deprecated/ir/inference/test_trt_inference_predictor_deprecated.py

@@ -96,14 +96,14 @@ def load(self, config_arg, inputs=None, outputs=None):
         elif self.args.precision == 'int8':
             precision_mode = paddle_infer.PrecisionType.Int8
 
-        if self.args.enable_mkldnn and not self.args.enable_gpu:
+        if self.args.enable_onednn and not self.args.enable_gpu:
             config.disable_gpu()
             config.enable_onednn()
             if self.args.precision == 'int8':
                 config.enable_onednn_int8(
                     {"conv2d", "depthwise_conv2d", "transpose2", "pool2d"}
                 )
-        if not self.args.enable_mkldnn and not self.args.enable_gpu:
+        if not self.args.enable_onednn and not self.args.enable_gpu:
             config.disable_gpu()
             # config.enable_onednn()
         if self.args.enable_profile:
@@ -251,7 +251,7 @@ def parse_args():
     parser.add_argument(
         '--paddle_params_file', type=str, default="model.pdiparams"
     )
-    parser.add_argument('--enable_mkldnn', type=str2bool, default=False)
+    parser.add_argument('--enable_onednn', type=str2bool, default=False)
     parser.add_argument('--enable_gpu', type=str2bool, default=True)
     parser.add_argument('--enable_trt', type=str2bool, default=True)
     parser.add_argument('--enable_dynamic_shape', type=str2bool, default=True)

test/ir/inference/test_onednn_batch_norm_act_fuse_pass.py

@@ -33,9 +33,9 @@ def sample_program_config(self, draw):
         momentum = draw(st.floats(min_value=0.0, max_value=5))
         trainable_statistics = False
         use_global_stats = draw(st.booleans())
-        use_mkldnn1 = draw(st.sampled_from([True]))
+        use_onednn1 = draw(st.sampled_from([True]))
         use_cudnn = draw(st.booleans())
-        use_mkldnn2 = draw(st.sampled_from([True]))
+        use_onednn2 = draw(st.sampled_from([True]))
         batch_size = draw(st.integers(min_value=1, max_value=4))
         channel = draw(st.integers(min_value=1, max_value=64))
         input_dim1 = draw(st.integers(min_value=1, max_value=512))
@@ -78,15 +78,15 @@ def generate_weight():
                 'momentum': momentum,
                 'trainable_statistics': trainable_statistics,
                 'use_global_stats': use_global_stats,
-                'use_mkldnn': use_mkldnn1,
+                'use_mkldnn': use_onednn1,
             },
         )
 
         relu_op = OpConfig(
             type='relu',
             inputs={'X': ['norm_output']},
             outputs={'Out': ['relu_output']},
-            attrs={'use_cudnn': use_cudnn, 'use_mkldnn': use_mkldnn2},
+            attrs={'use_cudnn': use_cudnn, 'use_mkldnn': use_onednn2},
         )
 
         model_net = [batch_norm_op, relu_op]