rm unittests eger guard tests part10 imperative_decorator2hook (#48827)

* rm unittests eger guard tests part10 imperative_decorator2hook

* modify

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

@@ -20,7 +20,6 @@ from test_imperative_base import new_program_scope
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.framework as framework
-from paddle.fluid.framework import _test_eager_guard
 
 
 class TestTracerMode(unittest.TestCase):
@@ -47,7 +46,7 @@ class TestTracerMode(unittest.TestCase):
         finally:
             self.assertEqual(rlt, ans)
 
-    def func_main(self):
+    def test_main(self):
         with fluid.dygraph.guard():
             self.tracer = framework._dygraph_tracer()
             self.tracer._train_mode = self.init_mode
@@ -73,11 +72,6 @@ class TestTracerMode(unittest.TestCase):
         with new_program_scope():
             self.check_not_support_rlt(True)
 
-    def test_main(self):
-        with _test_eager_guard():
-            self.func_main()
-        self.func_main()
-
 
 class TestTracerMode2(TestTracerMode):
     def setUp(self):
@@ -91,7 +85,7 @@ class TestNoGradClass(unittest.TestCase):
         self.assertEqual(self.tracer._has_grad, False)
         return a
 
-    def func_main(self):
+    def test_main(self):
         paddle.disable_static()
 
         self.tracer = framework._dygraph_tracer()
@@ -128,11 +122,6 @@ class TestNoGradClass(unittest.TestCase):
 
         self.assertEqual(a, b)
 
-    def test_main(self):
-        with _test_eager_guard():
-            self.func_main()
-        self.func_main()
-
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -24,7 +24,6 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.dygraph.base import to_variable
-from paddle.fluid.framework import _test_eager_guard
 from paddle.nn import Linear
 
 
@@ -375,7 +374,6 @@ class TestDygraphDeepCF(unittest.TestCase):
                     )
 
         with fluid.dygraph.guard():
-            with _test_eager_guard():
             paddle.seed(seed)
             paddle.framework.random._manual_program_seed(seed)
             fluid.default_startup_program().random_seed = seed
@@ -394,12 +392,8 @@ class TestDygraphDeepCF(unittest.TestCase):
                     if slice + self.batch_size >= users_np.shape[0]:
                         break
                     prediction = deepcf(
-                            to_variable(
-                                users_np[slice : slice + self.batch_size]
-                            ),
-                            to_variable(
-                                items_np[slice : slice + self.batch_size]
-                            ),
+                        to_variable(users_np[slice : slice + self.batch_size]),
+                        to_variable(items_np[slice : slice + self.batch_size]),
                     )
                     loss = paddle.sum(
                         paddle.nn.functional.log_loss(

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

@@ -20,7 +20,6 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 import paddle.nn.functional as F
-from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.wrapped_decorator import wrap_decorator
 from paddle.vision.models import resnet50, resnet101
 
@@ -45,7 +44,7 @@ def random_var(size, low=-1, high=1, dtype='float32'):
 
 
 class TestEagerGrad(TestCase):
-    def func_simple_example_eager_grad(self):
+    def test_simple_example_eager_grad(self):
         np.random.seed(2021)
         paddle.set_device('cpu')
         np_x = np.random.random((3, 3))
@@ -62,12 +61,7 @@ class TestEagerGrad(TestCase):
         self.assertEqual(dx[0].stop_gradient, True)
         np.testing.assert_allclose(dx[0].numpy(), expected_dx, rtol=1e-05)
 
-    def test_simple_example_eager_grad(self):
-        with _test_eager_guard():
-            self.func_simple_example_eager_grad()
-        self.func_simple_example_eager_grad()
-
-    def func_simple_example_eager_grad_allow_unused(self):
+    def test_simple_example_eager_grad_allow_unused(self):
         np.random.seed(2021)
         paddle.set_device('cpu')
         np_x = np.random.random((3, 3))
@@ -88,12 +82,7 @@ class TestEagerGrad(TestCase):
         # x is unused input in the graph
         self.assertIsNone(dx[1])
 
-    def test_simple_example_eager_grad_allow_unused(self):
-        with _test_eager_guard():
-            self.func_simple_example_eager_grad_allow_unused()
-        self.func_simple_example_eager_grad_allow_unused()
-
-    def func_simple_example_eager_grad_not_allow_unused(self):
+    def test_simple_example_eager_grad_not_allow_unused(self):
         np.random.seed(2021)
         paddle.set_device('cpu')
         np_x = np.random.random((3, 3))
@@ -112,12 +101,7 @@ class TestEagerGrad(TestCase):
             error_msg = str(e)
             assert error_msg.find("allow_unused") > 0
 
-    def test_simple_example_eager_grad_not_allow_unused(self):
-        with _test_eager_guard():
-            self.func_simple_example_eager_grad_not_allow_unused()
-        self.func_simple_example_eager_grad_not_allow_unused()
-
-    def func_simple_example_eager_grad_duplicate_input(self):
+    def test_simple_example_eager_grad_duplicate_input(self):
         np.random.seed(2021)
         paddle.set_device('cpu')
         np_x = np.random.random((3, 3))
@@ -136,12 +120,7 @@ class TestEagerGrad(TestCase):
             error_msg = str(e)
             assert error_msg.find("duplicate") > 0
 
-    def test_simple_example_eager_grad_duplicate_input(self):
-        with _test_eager_guard():
-            self.func_simple_example_eager_grad_duplicate_input()
-        self.func_simple_example_eager_grad_duplicate_input()
-
-    def func_simple_example_eager_grad_duplicate_output(self):
+    def test_simple_example_eager_grad_duplicate_output(self):
         np.random.seed(2021)
         paddle.set_device('cpu')
         np_x = np.random.random((3, 3))
@@ -160,13 +139,7 @@ class TestEagerGrad(TestCase):
             error_msg = str(e)
             assert error_msg.find("duplicate") > 0
 
-    def test_simple_example_eager_grad_duplicate_output(self):
-        with _test_eager_guard():
-            self.func_simple_example_eager_grad_duplicate_output()
-        self.func_simple_example_eager_grad_duplicate_output()
-
     def test_simple_example_eager_two_grad_output(self):
-        with _test_eager_guard():
         x1 = paddle.to_tensor([1.0, 2.0])
         x1.stop_gradient = False
         x2 = paddle.to_tensor([1.0, 2.0])
@@ -233,7 +206,7 @@ class TestDygraphDoubleGrad(TestCase):
         )
 
     @dygraph_guard
-    def func_exception(self):
+    def test_exception(self):
         with self.assertRaises(AssertionError):
             self.grad(None, None)
 
@@ -266,13 +239,8 @@ class TestDygraphDoubleGrad(TestCase):
         with self.assertRaises(AssertionError):
             self.grad([random_var(shape)], [random_var(shape)], no_grad_vars=1)
 
-    def test_exception(self):
-        with _test_eager_guard():
-            self.func_exception()
-        self.func_exception()
-
     @dygraph_guard
-    def func_simple_example(self):
+    def test_simple_example(self):
         x = random_var(self.shape)
         x.stop_gradient = False
         y = x + 1
@@ -306,13 +274,8 @@ class TestDygraphDoubleGrad(TestCase):
                 grad_with_none_and_not_none.stop_gradient, create_graph
             )
 
-    def test_simple_example(self):
-        with _test_eager_guard():
-            self.func_simple_example()
-        self.func_simple_example()
-
     @dygraph_guard
-    def func_example_no_grad_vars(self):
+    def test_example_no_grad_vars(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -343,13 +306,8 @@ class TestDygraphDoubleGrad(TestCase):
 
         np.testing.assert_allclose(dx_actual.numpy(), dx_expected, rtol=1e-05)
 
-    def test_example_no_grad_vars(self):
-        with _test_eager_guard():
-            self.func_example_no_grad_vars()
-        self.func_example_no_grad_vars()
-
     @dygraph_guard
-    def func_none_one_initial_gradient(self):
+    def test_none_one_initial_gradient(self):
         numel = 1
         for s in self.shape:
             numel *= s
@@ -425,13 +383,8 @@ class TestDygraphDoubleGrad(TestCase):
                             grad_z.numpy(), original_random_grad_z
                         )
 
-    def test_none_one_initial_gradient(self):
-        with _test_eager_guard():
-            self.func_none_one_initial_gradient()
-        self.func_none_one_initial_gradient()
-
     @dygraph_guard
-    def func_example_with_gradient_accumulation_and_create_graph(self):
+    def test_example_with_gradient_accumulation_and_create_graph(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -478,13 +431,8 @@ class TestDygraphDoubleGrad(TestCase):
                 x_grad_actual, x_grad_expected, rtol=1e-05
             )
 
-    def test_example_with_gradient_accumulation_and_create_graph(self):
-        with _test_eager_guard():
-            self.func_example_with_gradient_accumulation_and_create_graph()
-        self.func_example_with_gradient_accumulation_and_create_graph()
-
     @dygraph_guard
-    def func_example_with_gradient_accumulation_and_no_grad_vars(self):
+    def test_example_with_gradient_accumulation_and_no_grad_vars(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -529,13 +477,8 @@ class TestDygraphDoubleGrad(TestCase):
         ).astype('float32')
         np.testing.assert_allclose(x_grad_actual, x_grad_expected, rtol=1e-05)
 
-    def test_example_with_gradient_accumulation_and_no_grad_vars(self):
-        with _test_eager_guard():
-            self.func_example_with_gradient_accumulation_and_no_grad_vars()
-        self.func_example_with_gradient_accumulation_and_no_grad_vars()
-
     @dygraph_guard
-    def func_example_with_gradient_accumulation_and_not_create_graph(self):
+    def test_example_with_gradient_accumulation_and_not_create_graph(self):
         x = random_var(self.shape)
         x_np = x.numpy()
         numel = x_np.size
@@ -566,11 +509,6 @@ class TestDygraphDoubleGrad(TestCase):
         x_grad_expected = (2.0 * x_np / float(numel)).astype('float32')
         np.testing.assert_allclose(x_grad_actual, x_grad_expected, rtol=1e-05)
 
-    def test_example_with_gradient_accumulation_and_not_create_graph(self):
-        with _test_eager_guard():
-            self.func_example_with_gradient_accumulation_and_not_create_graph()
-        self.func_example_with_gradient_accumulation_and_not_create_graph()
-
 
 class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):
     def setUp(self):
@@ -579,7 +517,7 @@ class TestDygraphDoubleGradSortGradient(TestDygraphDoubleGrad):
 
 
 class TestDygraphDoubleGradVisitedUniq(TestCase):
-    def func_compare(self):
+    def test_compare(self):
         value = (
             np.random.uniform(-0.5, 0.5, 100)
             .reshape(10, 2, 5)
@@ -628,14 +566,9 @@ class TestDygraphDoubleGradVisitedUniq(TestCase):
 
         np.testing.assert_array_equal(grad_1, grad_2)
 
-    def test_compare(self):
-        with _test_eager_guard():
-            self.func_compare()
-        self.func_compare()
-
 
 class TestRaiseNoDoubleGradOp(TestCase):
-    def raise_no_grad_op(self):
+    def test_no_grad_op(self):
         with fluid.dygraph.guard():
             x = paddle.ones(shape=[2, 3, 2, 2], dtype='float32')
             x.stop_gradient = False
@@ -648,9 +581,6 @@ class TestRaiseNoDoubleGradOp(TestCase):
             loss = paddle.mean(dx)
             loss.backward()
 
-    def test_raise(self):
-        self.assertRaises(RuntimeError, self.raise_no_grad_op)
-
 
 class TestDoubleGradResNet(TestCase):
     def setUp(self):
@@ -660,7 +590,6 @@ class TestDoubleGradResNet(TestCase):
 
     @dygraph_guard
     def test_resnet_resnet50(self):
-        with _test_eager_guard():
         model = resnet50(pretrained=False)
         egr_data = paddle.to_tensor(self.data)
         egr_data.stop_gradient = False
@@ -694,7 +623,6 @@ class TestDoubleGradResNet(TestCase):
 
     @dygraph_guard
     def test_resnet_resnet101(self):
-        with _test_eager_guard():
         model = resnet101(pretrained=False)
         egr_data = paddle.to_tensor(self.data)
         egr_data.stop_gradient = False
@@ -730,13 +658,8 @@ class TestDoubleGradResNet(TestCase):
 class TestDoubleGradBasics(TestCase):
     def test_matmul(self):
         input_numpy = np.ones([3, 3]) * 2
-        with _test_eager_guard():
-            x = paddle.to_tensor(
-                input_numpy, stop_gradient=False, dtype='float32'
-            )
-            y = paddle.to_tensor(
-                input_numpy, stop_gradient=False, dtype='float32'
-            )
+        x = paddle.to_tensor(input_numpy, stop_gradient=False, dtype='float32')
+        y = paddle.to_tensor(input_numpy, stop_gradient=False, dtype='float32')
         grad_out = paddle.to_tensor(
             np.ones([3, 3]), stop_gradient=False, dtype='float32'
         )
@@ -762,9 +685,7 @@ class TestDoubleGradBasics(TestCase):
         np.testing.assert_array_equal(y.grad.numpy(), y_grad_ref)
 
         grad_out_grad_ref = np.ones([3, 3]) * 6.0
-            np.testing.assert_array_equal(
-                grad_out.grad.numpy(), grad_out_grad_ref
-            )
+        np.testing.assert_array_equal(grad_out.grad.numpy(), grad_out_grad_ref)
 
 
 if __name__ == '__main__':

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

@@ -19,7 +19,6 @@ from test_imperative_base import new_program_scope
 
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid.framework import _test_eager_guard
 
 
 class MLP(fluid.Layer):
@@ -54,7 +53,7 @@ class MLP(fluid.Layer):
 
 
 class TestDygraphFramework(unittest.TestCase):
-    def func_test_dygraph_backward(self):
+    def test_dygraph_backward(self):
         with new_program_scope():
             mlp = MLP(input_size=2)
             var_inp = fluid.layers.data(
@@ -69,18 +68,8 @@ class TestDygraphFramework(unittest.TestCase):
             except AssertionError as e:
                 self.assertTrue((e is not None))
 
-    def test_dygraph_backward(self):
-        with _test_eager_guard():
-            self.func_test_dygraph_backward()
-        self.func_test_dygraph_backward()
-
-    def func_test_dygraph_to_string(self):
+    def test_dygraph_to_string(self):
         np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
         with fluid.dygraph.guard():
             var_inp = fluid.dygraph.to_variable(np_inp)
             print(str(var_inp))
-
-    def test_dygraph_to_string(self):
-        with _test_eager_guard():
-            self.func_test_dygraph_to_string()
-        self.func_test_dygraph_to_string()

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

@@ -21,7 +21,6 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.dygraph.base import to_variable
-from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.optimizer import SGDOptimizer
 from paddle.nn import Linear
 
@@ -56,7 +55,7 @@ class Generator(fluid.Layer):
 
 
 class TestDygraphGAN(unittest.TestCase):
-    def func_test_gan_float32(self):
+    def test_gan_float32(self):
         seed = 90
         paddle.seed(1)
         paddle.framework.random._manual_program_seed(1)
@@ -271,11 +270,6 @@ class TestDygraphGAN(unittest.TestCase):
         for k, v in dy_params2.items():
             np.testing.assert_allclose(v, static_params[k], rtol=1e-05)
 
-    def test_gan_float32(self):
-        with _test_eager_guard():
-            self.func_test_gan_float32()
-        self.func_test_gan_float32()
-
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -23,7 +23,6 @@ import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.nn.functional as F
 from paddle.fluid.dygraph.base import to_variable
-from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.optimizer import AdamOptimizer
 
 
@@ -64,7 +63,7 @@ class GCN(fluid.Layer):
 
 
 class TestDygraphGNN(unittest.TestCase):
-    def func_gnn_float32(self):
+    def test_gnn_float32(self):
         paddle.seed(90)
         paddle.framework.random._manual_program_seed(90)
         startup = fluid.Program()
@@ -188,11 +187,6 @@ class TestDygraphGNN(unittest.TestCase):
         )
         sys.stderr.write('%s %s\n' % (static_loss, loss_value))
 
-    def test_gnn_float32(self):
-        with _test_eager_guard():
-            self.func_gnn_float32()
-        self.func_gnn_float32()
-
 
 if __name__ == '__main__':
     paddle.enable_static()

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

@@ -16,11 +16,7 @@ import unittest
 
 import paddle
 import paddle.fluid.core as core
-from paddle.fluid.framework import (
-    _in_legacy_dygraph,
-    _test_eager_guard,
-    in_dygraph_mode,
-)
+from paddle.fluid.framework import _in_legacy_dygraph, in_dygraph_mode
 
 
 class TestDataParallelGroup(unittest.TestCase):
@@ -170,20 +166,6 @@ class TestDataParallelGroup(unittest.TestCase):
         )
         self.assertEqual([[1, 0], [3], [2]], res)
 
-    def test_construct_group_in_legacy_mode(self):
-        with _test_eager_guard():
-            pass
-        self.test_construct_group0()
-        self.test_construct_group1()
-        self.test_construct_group2()
-        self.test_construct_group3()
-        self.test_construct_group4()
-        self.test_construct_group5()
-        self.test_construct_group6()
-        self.test_construct_group7()
-        self.test_construct_group8()
-        self.test_construct_group9()
-
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -20,7 +20,6 @@ from test_imperative_lod_tensor_to_selected_rows import SimpleNet
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.fluid.dygraph.base as base
-from paddle.fluid.framework import _test_eager_guard
 
 call_forward_post_hook = False
 call_forward_pre_hook = False
@@ -47,7 +46,7 @@ def forward_pre_hook1(layer, input):
 
 class Test_Forward_Hook(unittest.TestCase):
     # test forward_pre_hook and forward_post_hook that have return value
-    def func_forward_hook_return_value(self):
+    def test_forward_hook_return_value(self):
         seed = 90
 
         places = [fluid.CPUPlace()]
@@ -129,7 +128,7 @@ class Test_Forward_Hook(unittest.TestCase):
                 )
 
     # test forward_pre_hook and forward_post_hook that don't have return value
-    def func_forward_hook(self):
+    def test_forward_hook(self):
         seed = 90
 
         places = [fluid.CPUPlace()]
@@ -210,13 +209,6 @@ class Test_Forward_Hook(unittest.TestCase):
                 self.assertFalse(call_forward_post_hook)
                 self.assertFalse(call_forward_pre_hook)
 
-    def test_forward_hook_return_value(self):
-        with _test_eager_guard():
-            self.func_forward_hook()
-            self.func_forward_hook_return_value()
-        self.func_forward_hook()
-        self.func_forward_hook_return_value()
-
 
 if __name__ == '__main__':
     unittest.main()