update erf gumbel_softmax ..ops (#50077)

* update erf gumbel_softmax ..ops

* lint

* reset sequence_conv

* reset exponetial&interp nearest

python/paddle/fluid/tests/unittests/test_erf_op.py

@@ -15,7 +15,7 @@
 import unittest
 
 import numpy as np
-from op_test import OpTest
+from eager_op_test import OpTest
 from scipy.special import erf
 
 import paddle
@@ -26,6 +26,7 @@ import paddle.fluid.dygraph as dg
 class TestErfOp(OpTest):
     def setUp(self):
         self.op_type = "erf"
+        self.python_api = paddle.erf
         self.dtype = self._init_dtype()
         self.x_shape = [11, 17]
         x = np.random.uniform(-1, 1, size=self.x_shape).astype(self.dtype)

python/paddle/fluid/tests/unittests/test_expand_v2_op.py

@@ -17,7 +17,7 @@ import unittest
 import gradient_checker
 import numpy as np
 from decorator_helper import prog_scope
-from op_test import OpTest
+from eager_op_test import OpTest
 
 import paddle
 import paddle.fluid as fluid
@@ -42,10 +42,10 @@ class TestExpandV2OpRank1(OpTest):
         self.expand_times = [1]
 
     def test_check_output(self):
-        self.check_output(check_eager=True)
+        self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out', check_eager=True)
+        self.check_grad(['X'], 'Out')
 
 
 class TestExpandV2OpRank2_DimExpanding(TestExpandV2OpRank1):
@@ -80,6 +80,7 @@ class TestExpandV2OpRank4(TestExpandV2OpRank1):
 class TestExpandV2OpRank1_tensor_attr(OpTest):
     def setUp(self):
         self.op_type = "expand_v2"
+        self.python_api = paddle.expand
         self.init_data()
         expand_shapes_tensor = []
         for index, ele in enumerate(self.expand_shape):
@@ -120,6 +121,7 @@ class TestExpandV2OpRank2_Corner_tensor_attr(TestExpandV2OpRank1_tensor_attr):
 class TestExpandV2OpRank1_tensor(OpTest):
     def setUp(self):
         self.op_type = "expand_v2"
+        self.python_api = paddle.expand
         self.init_data()
 
         self.inputs = {
@@ -146,6 +148,7 @@ class TestExpandV2OpRank1_tensor(OpTest):
 class TestExpandV2OpInteger(OpTest):
     def setUp(self):
         self.op_type = "expand_v2"
+        self.python_api = paddle.expand
         self.inputs = {
             'X': np.random.randint(10, size=(2, 4, 5)).astype("int32")
         }
@@ -161,6 +164,7 @@ class TestExpandV2OpInteger(OpTest):
 class TestExpandV2OpBoolean(OpTest):
     def setUp(self):
         self.op_type = "expand_v2"
+        self.python_api = paddle.expand
         self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
         self.attrs = {'shape': [2, 4, 5]}
         output = np.tile(self.inputs['X'], (1, 1, 1))
@@ -174,6 +178,7 @@ class TestExpandV2OpBoolean(OpTest):
 class TestExpandV2OpInt64_t(OpTest):
     def setUp(self):
         self.op_type = "expand_v2"
+        self.python_api = paddle.expand
         self.inputs = {
             'X': np.random.randint(10, size=(2, 4, 5)).astype("int64")
         }

python/paddle/fluid/tests/unittests/test_gumbel_softmax_op.py

@@ -13,10 +13,11 @@
 import unittest
 
 import numpy as np
-from op_test import OpTest
+from eager_op_test import OpTest
 
 import paddle
 import paddle.fluid as fluid
+import paddle.nn.functional as F
 
 paddle.enable_static()
 
@@ -36,6 +37,7 @@ class TestGumbelSoftmaxOp(OpTest):
 
     def setUp(self):
         self.op_type = "gumbel_softmax"
+        self.python_api = F.gumbel_softmax
         self.init_attrs()
         np.random.seed(0)
         x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
@@ -53,6 +55,7 @@ class TestGumbelSoftmaxOp(OpTest):
 class TestGumbelSoftmax_ZeroDim(OpTest):
     def setUp(self):
         self.op_type = "gumbel_softmax"
+        self.python_api = F.gumbel_softmax
         self.dtype = "float64"
         x = np.random.uniform(0.1, 1, []).astype(self.dtype)
         out = np.array(1.0).astype(self.dtype)
@@ -123,6 +126,7 @@ class TestGumbelSoftmaxOpSampleDistribution(OpTest):
 
     def setUp(self):
         self.op_type = "gumbel_softmax"
+        self.python_api = F.gumbel_softmax
         self.init_attrs()
         single_x = np.array([0.2, 0.3, 0.5])
         batch_x = np.ones(self.shape) * single_x

python/paddle/fluid/tests/unittests/test_is_empty_op.py

@@ -15,7 +15,7 @@
 import unittest
 
 import numpy as np
-from op_test import OpTest
+from eager_op_test import OpTest
 
 import paddle
 
@@ -23,6 +23,7 @@ import paddle
 class TestEmpty(OpTest):
     def setUp(self):
         self.op_type = "is_empty"
+        self.python_api = paddle.is_empty
         self.inputs = {'X': np.array([1, 2, 3])}
         self.outputs = {'Out': np.array([False])}
 
@@ -33,6 +34,7 @@ class TestEmpty(OpTest):
 class TestNotEmpty(TestEmpty):
     def setUp(self):
         self.op_type = "is_empty"
+        self.python_api = paddle.is_empty
         self.inputs = {'X': np.array([])}
         self.outputs = {'Out': np.array([True])}
 

python/paddle/fluid/tests/unittests/test_multiplex_op.py

@@ -15,7 +15,7 @@
 import unittest
 
 import numpy as np
-from op_test import OpTest
+from eager_op_test import OpTest
 
 import paddle
 import paddle.fluid as fluid
@@ -24,6 +24,7 @@ import paddle.fluid as fluid
 class TestMultiplexOp(OpTest):
     def setUp(self):
         self.op_type = "multiplex"
+        self.python_api = paddle.tensor.multiplex
         rows = 4
         index = np.arange(0, rows).astype('int32')
         np.random.shuffle(index)

python/paddle/fluid/tests/unittests/test_transfer_layout_op.py

@@ -15,7 +15,7 @@
 import unittest
 
 import numpy as np
-from op_test import OpTest
+from eager_op_test import OpTest
 
 import paddle
 import paddle.fluid as fluid
@@ -24,6 +24,10 @@ from paddle.fluid.framework import Program, program_guard
 from paddle.fluid.layer_helper import LayerHelper
 
 
+def transpose_layout(x, src_layout, dst_layout):
+    return x.transpose([0, 2, 3, 1])
+
+
 # default kNCHW
 class TestTransferLayoutOpkNCHWTokNHWC(OpTest):
     def setUp(self):
@@ -31,6 +35,7 @@ class TestTransferLayoutOpkNCHWTokNHWC(OpTest):
         self.inputs = {'X': ipt.astype('float32')}
         self.outputs = {'Out': ipt.transpose([0, 2, 3, 1])}
         self.attrs = {'src_layout': 0, 'dst_layout': 1}  # kNHWC
+        self.python_api = transpose_layout
         self.op_type = 'transfer_layout'
 
     def test_check_output(self):

python/paddle/fluid/tests/unittests/test_transpose_op.py

@@ -17,12 +17,12 @@ import unittest
 import gradient_checker
 import numpy as np
 from decorator_helper import prog_scope
+from eager_op_test import OpTest, convert_float_to_uint16
 
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid import Program, program_guard
-from paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16
 
 paddle.enable_static()
 
@@ -47,10 +47,10 @@ class TestTransposeOp(OpTest):
         self.use_mkldnn = False
 
     def test_check_output(self):
-        self.check_output(no_check_set=['XShape'], check_eager=True)
+        self.check_output(no_check_set=['XShape'])
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out', check_eager=True)
+        self.check_grad(['X'], 'Out')
 
     def initTestCase(self):
         self.shape = (3, 40)
@@ -150,11 +150,11 @@ class TestAutoTuneTransposeOp(OpTest):
         self.use_mkldnn = False
 
     def test_check_output(self):
-        self.check_output(no_check_set=['XShape'], check_eager=True)
+        self.check_output(no_check_set=['XShape'])
         fluid.core.disable_autotune()
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Out', check_eager=True)
+        self.check_grad(['X'], 'Out')
 
 
 class TestTransposeBF16Op(OpTest):
@@ -162,6 +162,7 @@ class TestTransposeBF16Op(OpTest):
         self.init_op_type()
         self.initTestCase()
         self.dtype = np.uint16
+        self.python_api = paddle.transpose
         x = np.random.random(self.shape).astype("float32")
 
         self.inputs = {'X': convert_float_to_uint16(x)}
@@ -580,6 +581,7 @@ class TestTransposeAPI_ZeroDim(unittest.TestCase):
         x = paddle.rand([])
         x.stop_gradient = False
         out = paddle.transpose(x, [])
+        if hasattr(out, 'retain_grads'):
             out.retain_grads()
         out.backward()