[Yaml] add unittest for prelu, gelu. (#41444)

* add gelu pythonapi and unittest

* fix prelu

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

@@ -21,6 +21,7 @@ import paddle.fluid as fluid
 import paddle.fluid.dygraph as dg
 import paddle
 import paddle.nn.functional as F
+from paddle.fluid.framework import _test_eager_guard
 
 
 def gelu(x, approximate):
@@ -91,6 +92,10 @@ class TestGeluOp(unittest.TestCase):
             np.allclose(
                 x_g_ref, x_g_fast_math, rtol=1e-5, atol=5e-4))
 
+    def test_fast_math_eager(self):
+        with _test_eager_guard():
+            self.test_fast_math()
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -23,6 +23,7 @@ from paddle.fluid import Program, program_guard
 from op_test import OpTest, skip_check_grad_ci
 import paddle
 import paddle.nn.functional as F
+from paddle.fluid.framework import _test_eager_guard
 
 
 def ref_prelu(x, weight):
@@ -76,6 +77,10 @@ class TestFunctionalPReluAPI(unittest.TestCase):
         self.dygraph_check(self.weight_np_0)
         self.dygraph_check(self.weight_np_1)
 
+    def test_dygraph_api_eager(self):
+        with _test_eager_guard():
+            self.test_dygraph_api()
+
     def test_error(self):
         with paddle.static.program_guard(paddle.static.Program()):
             weight_fp32 = paddle.fluid.data(
@@ -151,13 +156,19 @@ class TestNNPReluAPI(unittest.TestCase):
         paddle.enable_static()
 
 
+def prelu_api_wrapper(x, weight, data_format="NCHW"):
+    weight = weight.reshape([-1])
+    return paddle.nn.functional.prelu(x, weight, data_format, name=None)
+
+
 class PReluTest(OpTest):
     def setUp(self):
         self.init_dtype()
         self.init_input_shape()
+        self.eager_mode = True
         self.init_attr()
         self.op_type = "prelu"
-        self.python_api = paddle.nn.functional.prelu
+        self.python_api = prelu_api_wrapper
 
         x_np = np.random.uniform(-1, 1, self.x_shape).astype(self.dtype)
         # Since zero point in prelu is not differentiable, avoid randomize
@@ -178,6 +189,8 @@ class PReluTest(OpTest):
             alpha_np = np.random.uniform(-1, -0.5, [1, 1, 1, self.x_shape[-1]])
         else:
             alpha_np = np.random.uniform(-1, -0.5, [1] + self.x_shape[1:])
+            # eager check don't support mode = 'all'
+            self.eager_mode = False
         alpha_np = alpha_np.astype(self.dtype)
 
         self.inputs = {'X': x_np, 'Alpha': alpha_np}
@@ -208,10 +221,10 @@ class PReluTest(OpTest):
         self.attrs = {'mode': "channel", "data_format": "NCHW"}
 
     def test_check_output(self):
-        self.check_output(check_eager=False)
+        self.check_output(check_eager=self.eager_mode)
 
     def test_check_grad(self):
-        self.check_grad(['X', 'Alpha'], 'Out', check_eager=False)
+        self.check_grad(['X', 'Alpha'], 'Out', check_eager=self.eager_mode)
 
 
 @skip_check_grad_ci(
@@ -375,7 +388,7 @@ def create_test_fp16_class(parent,
                 place = core.CUDAPlace(0)
                 if core.is_float16_supported(place):
                     self.check_output_with_place(
-                        place, atol=atol, check_eager=False)
+                        place, atol=atol, check_eager=self.eager_mode)
 
         def test_check_grad(self):
             place = core.CUDAPlace(0)
@@ -384,7 +397,7 @@ def create_test_fp16_class(parent,
                     place, ['X', 'Alpha'],
                     'Out',
                     max_relative_error=max_relative_error,
-                    check_eager=False)
+                    check_eager=self.eager_mode)
 
     cls_name = "{0}_{1}".format(parent.__name__, "Fp16Op")
     TestPReluFp16Case.__name__ = cls_name

python/paddle/nn/functional/activation.py

@@ -175,7 +175,10 @@ def gelu(x, approximate=False, name=None):
             #  [ 0.84119201,  1.39957154]]
     """
 
-    if in_dynamic_mode():
+    if in_dygraph_mode():
+        return _C_ops.final_state_gelu(x, approximate)
+
+    if _in_legacy_dygraph():
         return _C_ops.gelu(x, 'approximate', approximate)
 
     check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'gelu')