From 539fb0d7f551b06de5af84f2b40c3ca65bd1a7f5 Mon Sep 17 00:00:00 2001
From: crystal <62974595+Zjq9409@users.noreply.github.com>
Date: Thu, 24 Feb 2022 16:17:18 +0800
Subject: [PATCH] Fix unittests for eigh op (#39568)

* fix eigh test

* modify atol and rtol
---
 .../fluid/tests/unittests/test_eigh_op.py     | 41 ++++++++++++-------
 1 file changed, 26 insertions(+), 15 deletions(-)

diff --git a/python/paddle/fluid/tests/unittests/test_eigh_op.py b/python/paddle/fluid/tests/unittests/test_eigh_op.py
index 8e8c9df199..3e8230e5d0 100644
--- a/python/paddle/fluid/tests/unittests/test_eigh_op.py
+++ b/python/paddle/fluid/tests/unittests/test_eigh_op.py
@@ -59,8 +59,12 @@ class TestEighGPUCase(unittest.TestCase):
         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():
@@ -79,23 +83,30 @@ class TestEighGPUCase(unittest.TestCase):
 
 class TestEighAPI(unittest.TestCase):
     def setUp(self):
-        self.init_input_shape()
-        self.dtype = "float32"
+        self.init_input_data()
         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)
+
+    def init_input_data(self):
+        self.x_shape = [5, 5]
+        self.dtype = "float32"
         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]
+        #build a random conjugate matrix
+        self.complex_symm = np.divide(
+            complex_data + np.conj(complex_data.transpose(self.trans_dims)), 2)
 
     def compare_result(self, actual_w, actual_v, expected_w, expected_v):
         np.testing.assert_allclose(
@@ -129,9 +140,9 @@ class TestEighAPI(unittest.TestCase):
             exe = paddle.static.Executor(self.place)
             expected_w, expected_v = exe.run(
                 main_prog,
-                feed={"input_x": self.complex_data},
+                feed={"input_x": self.complex_symm},
                 fetch_list=[output_w, output_v])
-            actual_w, actual_v = np.linalg.eigh(self.complex_data)
+            actual_w, actual_v = np.linalg.eigh(self.complex_symm)
             self.compare_result(actual_w, actual_v, expected_w, expected_v)
 
     def test_in_static_mode(self):
@@ -146,14 +157,14 @@ class TestEighAPI(unittest.TestCase):
         actual_w, actual_v = paddle.linalg.eigh(input_real_data)
         self.compare_result(actual_w, actual_v.numpy(), expected_w, expected_v)
 
-        input_complex_data = paddle.to_tensor(self.complex_data)
-        expected_w, expected_v = np.linalg.eigh(self.complex_data)
+        input_complex_data = paddle.to_tensor(self.complex_symm)
+        expected_w, expected_v = np.linalg.eigh(self.complex_symm)
         actual_w, actual_v = paddle.linalg.eigh(input_complex_data)
         self.compare_result(actual_w, actual_v.numpy(), expected_w, expected_v)
 
     def test_eigh_grad(self):
         paddle.disable_static()
-        x = paddle.to_tensor(self.complex_data, stop_gradient=False)
+        x = paddle.to_tensor(self.complex_symm, stop_gradient=False)
         w, v = paddle.linalg.eigh(x)
         (w.sum() + paddle.abs(v).sum()).backward()
         np.testing.assert_allclose(
-- 
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