Rename use_mkldnn to use_onednn in test cases - part8

test/mkldnn/test_elementwise_add_bf16_mkldnn_op.py

@@ -27,16 +27,16 @@
 class TestElementwiseAddBf16MklDNNOp(OpTest):
  def setUp(self):
  self.op_type = "elementwise_add"
- self.use_mkldnn = True
- self.mkldnn_data_type = "bfloat16"
+ self.use_onednn = True
+ self.onednn_data_type = "bfloat16"
  self.axis = -1
 
  self.generate_data()
  self.x_bf16 = convert_float_to_uint16(self.x)
  self.y_bf16 = convert_float_to_uint16(self.y)
 
  self.inputs = {'X': self.x_bf16, 'Y': self.y_bf16}
- self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
+ self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_onednn}
  self.outputs = {'Out': convert_float_to_uint16(self.out)}
 
  def generate_data(self):

test/mkldnn/test_elementwise_add_mkldnn_op.py

@@ -23,7 +23,7 @@
 
 class TestOneDNNElementwiseAddOp(TestElementwiseAddOp):
  def init_kernel_type(self):
- self.use_mkldnn = True
+ self.use_onednn = True
  self.check_pir_onednn = True
 
  def init_dtype(self):
@@ -131,7 +131,7 @@ def init_input_output(self):
 )
 class TestInt8(TestElementwiseAddOp):
  def init_kernel_type(self):
- self.use_mkldnn = True
+ self.use_onednn = True
  self._cpu_only = True
  self.check_pir_onednn = True
 
@@ -152,7 +152,7 @@ def test_check_output(self):
  # TODO(wangzhongpu): support onednn op in dygraph mode
  self.init_scales()
  self.check_output(
- check_dygraph=(not self.use_mkldnn),
+ check_dygraph=(not self.use_onednn),
  check_pir_onednn=self.check_pir_onednn,
  )
 

test/mkldnn/test_elementwise_add_onednn_op_rare_shape.py

@@ -30,7 +30,7 @@
 # Special cases for swin transformer, will ignore grad check
 class TestOneDNNElementwiseAddSrcDifferentShape(TestElementwiseAddOp):
  def init_kernel_type(self):
- self.use_mkldnn = True
+ self.use_onednn = True
  self.check_pir_onednn = True
 
  def init_dtype(self):

test/mkldnn/test_elementwise_div_mkldnn_op.py

@@ -37,7 +37,7 @@ def setUp(self):
  'X': OpTest.np_dtype_to_base_dtype(self.x),
  'Y': OpTest.np_dtype_to_base_dtype(self.y),
  }
- self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
+ self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_onednn}
  self.outputs = {'Out': self.out}
 
  def init_input_output(self):
@@ -64,7 +64,7 @@ def init_axis(self):
  self.axis = -1
 
  def init_kernel_type(self):
- self.use_mkldnn = True
+ self.use_onednn = True
 
  def init_dtype(self):
  self.dtype = np.float32
@@ -164,12 +164,12 @@ def setUp(self):
  self.x_bf16 = convert_float_to_uint16(self.x)
  self.y_bf16 = convert_float_to_uint16(self.y)
  self.inputs = {'X': self.x_bf16, 'Y': self.y_bf16}
- self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
+ self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_onednn}
  self.outputs = {'Out': convert_float_to_uint16(self.out)}
 
  def init_dtype(self):
  self.dtype = np.float32
- self.mkldnn_data_type = "bfloat16"
+ self.onednn_data_type = "bfloat16"
 
  def init_input_output(self):
  self.x = np.random.uniform(0.1, 1, [100]).astype(self.dtype)

test/mkldnn/test_elementwise_mul_bf16_mkldnn_op.py

@@ -27,15 +27,15 @@
 class TestElementwiseMulBf16MklDNNOp(OpTest):
  def setUp(self):
  self.op_type = "elementwise_mul"
- self.use_mkldnn = True
- self.mkldnn_data_type = "bfloat16"
+ self.use_onednn = True
+ self.onednn_data_type = "bfloat16"
  self.axis = -1
 
  self.generate_data()
  self.x_bf16 = convert_float_to_uint16(self.x)
  self.y_bf16 = convert_float_to_uint16(self.y)
  self.inputs = {'X': self.x_bf16, 'Y': self.y_bf16}
- self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
+ self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_onednn}
  self.outputs = {'Out': convert_float_to_uint16(self.out)}
 
  def generate_data(self):