use large input size in test_add_position_encoding_op.py (#21962)

* use large input size in test_add_position_encoding_op.py, test=develop

* revert shape_white_list, test=develop

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

@@ -53,7 +53,7 @@ class TestAddPositionEncodingTensorOp(OpTest):
         """
         self.alpha = 0.6
         self.beta = 0.5
-        self.x = np.random.uniform(0.1, 1, [2, 4, 4]).astype(self.dtype)
+        self.x = np.random.uniform(0.1, 1, [2, 15, 4]).astype(self.dtype)
         self.out = np.copy(self.x)
 
         batch_size = self.x.shape[0]
@@ -64,7 +64,7 @@ class TestAddPositionEncodingTensorOp(OpTest):
         for i in range(batch_size):
             for j in range(max_length):
                 for k in range(half_shape):
-                    val = j / pow(10000.0, k / (
+                    val = j / pow(10000.0, k * 1.0 / (
                         half_shape - 1)) if half_shape > 1 else j / 10000.0
                     self.out[i, j, k] = \
                         self.x[i, j, k] * self.alpha + math.sin(val) * self.beta
@@ -107,8 +107,8 @@ class TestAddPositionEncodingLoDTensorOp(OpTest):
         """
         self.alpha = 0.6
         self.beta = 0.5
-        self.x = np.random.uniform(0.1, 1, [10, 4]).astype(self.dtype)
-        self.lod = [[3, 7]]
+        self.x = np.random.uniform(0.1, 1, [20, 6]).astype(self.dtype)
+        self.lod = [[13, 7]]
         self.out = np.copy(self.x)
 
         batch_size = len(self.lod[0])
@@ -120,7 +120,7 @@ class TestAddPositionEncodingLoDTensorOp(OpTest):
             max_length = self.lod[0][i]
             for j in range(max_length):
                 for k in range(half_shape):
-                    val = j / pow(10000.0, k / (
+                    val = j / pow(10000.0, k * 1.0 / (
                         half_shape - 1)) if half_shape > 1 else j / 10000.0
                     pos = start + j
                     self.out[pos, k] = \