Update unit tests by using _test_eager_guard (#40760)

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

@@ -17,10 +17,11 @@ from __future__ import print_function
 import unittest
 import paddle.fluid as fluid
 import numpy as np
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TestImperativePartitialBackward(unittest.TestCase):
-    def test_partitial_backward(self):
+    def func_partitial_backward(self):
         with fluid.dygraph.guard():
             x = np.random.randn(2, 4, 5).astype("float32")
             x = fluid.dygraph.to_variable(x)
@@ -49,6 +50,11 @@ class TestImperativePartitialBackward(unittest.TestCase):
             linear1.clear_gradients()
             linear2.clear_gradients()
 
+    def test_partitial_backward(self):
+        with _test_eager_guard():
+            self.func_partitial_backward()
+        self.func_partitial_backward()
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -20,6 +20,8 @@ import numpy as np
 import paddle
 import paddle.nn as nn
 from paddle.fluid.framework import _test_eager_guard, _in_eager_mode
+import paddle.fluid as fluid
+import paddle.fluid.core as core
 
 
 class SimpleNet(nn.Layer):
@@ -445,8 +447,7 @@ class TestTensorRegisterHook(unittest.TestCase):
             self.func_multiple_hooks_for_interior_var()
         self.func_multiple_hooks_for_interior_var()
 
-    # TODO(wuweilong): enable this case when DoubleGrad in eager mode is ready
-    def test_hook_in_double_grad(self):
+    def func_hook_in_double_grad(self):
         def double_print_hook(grad):
             grad = grad * 2
             print(grad)
@@ -461,10 +462,11 @@ class TestTensorRegisterHook(unittest.TestCase):
         x.register_hook(double_print_hook)
 
         y = x * x
-
+        fluid.set_flags({'FLAGS_retain_grad_for_all_tensor': False})
         # Since y = x * x, dx = 2 * x
         dx = paddle.grad(
             outputs=[y], inputs=[x], create_graph=True, retain_graph=True)[0]
+        fluid.set_flags({'FLAGS_retain_grad_for_all_tensor': True})
 
         z = y + dx
         self.assertTrue(x.grad is None)
@@ -475,9 +477,18 @@ class TestTensorRegisterHook(unittest.TestCase):
         # x.gradient() = 2 * x + 2 = 4.0
         # after changed by hook: 8.0
 
+        # TODO(wuweilong): enable this case when DoubleGrad in eager mode is ready
+        if core._in_eager_mode():
+            pass
+        else:
             z.backward()
             self.assertTrue(np.array_equal(x.grad.numpy(), np.array([8.])))
 
+    def test_hook_in_double_grad(self):
+        with _test_eager_guard():
+            self.func_hook_in_double_grad()
+        self.func_hook_in_double_grad()
+
     def func_remove_one_hook_multiple_times(self):
         for device in self.devices:
             paddle.set_device(device)