fix unittests for eignvalsh

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

@@ -60,8 +60,12 @@ def setUp(self):
         self.dtype = "float32"
         np.random.seed(123)
         self.x_np = np.random.random(self.x_shape).astype(self.dtype)
-        self.rtol = 1e-5
-        self.atol = 1e-5
+        if (paddle.version.cuda() >= "11.6"):
+            self.rtol = 5e-6
+            self.atol = 6e-5
+        else:
+            self.rtol = 1e-5
+            self.atol = 1e-5
 
     def test_check_output_gpu(self):
         if paddle.is_compiled_with_cuda():
@@ -75,23 +79,29 @@ def test_check_output_gpu(self):
 
 class TestEigvalshAPI(unittest.TestCase):
     def setUp(self):
-        self.init_input_shape()
+        self.x_shape = [5, 5]
         self.dtype = "float32"
         self.UPLO = 'L'
-        self.rtol = 1e-6
-        self.atol = 1e-6
+        if (paddle.version.cuda() >= "11.6"):
+            self.rtol = 5e-6
+            self.atol = 6e-5
+        else:
+            self.rtol = 1e-5
+            self.atol = 1e-5
         self.place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() \
             else paddle.CPUPlace()
         np.random.seed(123)
+        self.init_input_data()
+
+    def init_input_data(self):
         self.real_data = np.random.random(self.x_shape).astype(self.dtype)
-        self.complex_data = np.random.random(self.x_shape).astype(
+        complex_data = np.random.random(self.x_shape).astype(
             self.dtype) + 1J * np.random.random(self.x_shape).astype(self.dtype)
         self.trans_dims = list(range(len(self.x_shape) - 2)) + [
             len(self.x_shape) - 1, len(self.x_shape) - 2
         ]
-
-    def init_input_shape(self):
-        self.x_shape = [5, 5]
+        self.complex_symm = np.divide(
+            complex_data + np.conj(complex_data.transpose(self.trans_dims)), 2)
 
     def compare_result(self, actual_w, expected_w):
         np.testing.assert_allclose(
@@ -122,9 +132,9 @@ def check_static_complex_result(self):
             output_w = paddle.linalg.eigvalsh(input_x)
             exe = paddle.static.Executor(self.place)
             expected_w = exe.run(main_prog,
-                                 feed={"input_x": self.complex_data},
+                                 feed={"input_x": self.complex_symm},
                                  fetch_list=[output_w])
-            actual_w = np.linalg.eigvalsh(self.complex_data)
+            actual_w = np.linalg.eigvalsh(self.complex_symm)
             self.compare_result(actual_w, expected_w[0])
 
     def test_in_static_mode(self):
@@ -139,14 +149,14 @@ def test_in_dynamic_mode(self):
         actual_w = paddle.linalg.eigvalsh(input_real_data)
         self.compare_result(actual_w, expected_w)
 
-        input_complex_data = paddle.to_tensor(self.complex_data)
-        expected_w = np.linalg.eigvalsh(self.complex_data)
-        actual_w = paddle.linalg.eigvalsh(input_complex_data)
+        input_complex_symm = paddle.to_tensor(self.complex_symm)
+        expected_w = np.linalg.eigvalsh(self.complex_symm)
+        actual_w = paddle.linalg.eigvalsh(input_complex_symm)
         self.compare_result(actual_w, expected_w)
 
     def test_eigvalsh_grad(self):
         paddle.disable_static(self.place)
-        x = paddle.to_tensor(self.complex_data, stop_gradient=False)
+        x = paddle.to_tensor(self.complex_symm, stop_gradient=False)
         w = paddle.linalg.eigvalsh(x)
         (w.sum()).backward()
         np.testing.assert_allclose(