rm unittests eager guard tests part21 sum_op2tensor_zero (#48883)

* rm unittests eager guard tests part21 sum_op2tensor_zero

* modify tensor hook test

* rm assert for type promotion

* review

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

@@ -26,7 +26,6 @@ import paddle.fluid.core as core
 import paddle.fluid.layers as layers
 import paddle.inference as paddle_infer
 from paddle import enable_static
-from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.op import Operator
 from paddle.fluid.tests.unittests.op_test import (
     OpTest,
@@ -376,7 +375,6 @@ class API_Test_Add_n(unittest.TestCase):
 
     def test_dygraph_api(self):
         with fluid.dygraph.guard():
-            with _test_eager_guard():
             input0 = paddle.ones(shape=[2, 3], dtype='float32')
             input1 = paddle.ones(shape=[2, 3], dtype='float32')
             input0.stop_gradient = False
@@ -384,9 +382,7 @@ class API_Test_Add_n(unittest.TestCase):
             expected_result = np.empty((2, 3))
             expected_result.fill(2)
             sum_value = paddle.add_n([input0, input1])
-                self.assertEqual(
-                    (sum_value.numpy() == expected_result).all(), True
-                )
+            self.assertEqual((sum_value.numpy() == expected_result).all(), True)
 
             expected_grad_result = np.empty((2, 3))
             expected_grad_result.fill(1)

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

@@ -112,21 +112,11 @@ class TestDygraphAutoTuneStatus(TestAutoTune):
             expected_res = self.get_expected_res(i, enable_autotune)
             self.check_status(expected_res)
 
-    def func_enable_autotune(self):
-        self.run_program(enable_autotune=True)
-
     def test_enable_autotune(self):
-        with paddle.fluid.framework._test_eager_guard():
-            self.func_enable_autotune()
-        self.func_enable_autotune()
-
-    def func_disable_autotune(self):
-        self.run_program(enable_autotune=False)
+        self.run_program(enable_autotune=True)
 
     def test_disable_autotune(self):
-        with paddle.fluid.framework._test_eager_guard():
-            self.func_disable_autotune()
-        self.func_disable_autotune()
+        self.run_program(enable_autotune=False)
 
 
 class TestStaticAutoTuneStatus(TestAutoTune):

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

@@ -18,14 +18,13 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid.framework import _test_eager_guard
 
 
 class TensorFill_Test(unittest.TestCase):
     def setUp(self):
         self.shape = [32, 32]
 
-    def func_test_tensor_fill_true(self):
+    def test_tensor_fill_true(self):
         typelist = ['float32', 'float64', 'int32', 'int64', 'float16']
         places = [fluid.CPUPlace()]
         if fluid.core.is_compiled_with_cuda():
@@ -49,12 +48,8 @@ class TensorFill_Test(unittest.TestCase):
                 tensor.fill_(var)  # var type is basic type in typelist
                 self.assertEqual((tensor.numpy() == target).all(), True)
 
-    def test_tensor_fill_true(self):
-        with _test_eager_guard():
-            self.func_test_tensor_fill_true()
-        self.func_test_tensor_fill_true()
-
-    def func_test_tensor_fill_backward(self):
+    def test_tensor_fill_backward(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         typelist = ['float32']
         places = [fluid.CPUPlace()]
         if fluid.core.is_compiled_with_cuda():
@@ -79,26 +74,15 @@ class TensorFill_Test(unittest.TestCase):
                 loss.backward()
 
                 self.assertEqual((y.grad.numpy() == 0).all().item(), True)
-
-    def test_tensor_fill_backward(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_test_tensor_fill_backward()
-        self.func_test_tensor_fill_backward()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_test_errors(self):
+    def test_errors(self):
         def test_list():
             x = paddle.to_tensor([2, 3, 4])
             x.fill_([1])
 
         self.assertRaises(TypeError, test_list)
 
-    def test_errors(self):
-        with _test_eager_guard():
-            self.func_test_errors()
-        self.func_test_errors()
-
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -18,11 +18,11 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid.framework import _test_eager_guard
 
 
 class TensorFillDiagonal_Test(unittest.TestCase):
-    def func_dim2_normal(self):
+    def test_dim2_normal(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         expected_np = np.array([[1, 2, 2], [2, 1, 2], [2, 2, 1]]).astype(
             'float32'
         )
@@ -55,15 +55,10 @@ class TensorFillDiagonal_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True,
                 )
-
-    def test_dim2_normal(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_dim2_normal()
-        self.func_dim2_normal()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_offset(self):
+    def test_offset(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         expected_np = np.array([[2, 2, 1], [2, 2, 2], [2, 2, 2]]).astype(
             'float32'
         )
@@ -96,15 +91,9 @@ class TensorFillDiagonal_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True,
                 )
-
-    def test_offset(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_offset()
-        self.func_offset()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_bool(self):
+    def test_bool(self):
         expected_np = np.array(
             [[False, True, True], [True, False, True], [True, True, False]]
         )
@@ -126,12 +115,8 @@ class TensorFillDiagonal_Test(unittest.TestCase):
 
                 self.assertEqual((x.numpy() == expected_np).all(), True)
 
-    def test_bool(self):
-        with _test_eager_guard():
-            self.func_bool()
-        self.func_bool()
-
-    def func_dim2_unnormal_wrap(self):
+    def test_dim2_unnormal_wrap(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         expected_np = np.array(
             [
                 [1, 2, 2],
@@ -180,15 +165,10 @@ class TensorFillDiagonal_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True,
                 )
-
-    def test_dim2_unnormal_wrap(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_dim2_unnormal_wrap()
-        self.func_dim2_unnormal_wrap()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_dim2_unnormal_unwrap(self):
+    def test_dim2_unnormal_unwrap(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         expected_np = np.array(
             [
                 [1, 2, 2],
@@ -237,15 +217,10 @@ class TensorFillDiagonal_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True,
                 )
-
-    def test_dim2_unnormal_unwrap(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_dim2_unnormal_unwrap()
-        self.func_dim2_unnormal_unwrap()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_dim_larger2_normal(self):
+    def test_dim_larger2_normal(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         expected_np = np.array(
             [
                 [[1, 2, 2], [2, 2, 2], [2, 2, 2]],
@@ -286,12 +261,6 @@ class TensorFillDiagonal_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True,
                 )
-
-    def test_dim_larger2_normal(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_dim_larger2_normal()
-        self.func_dim_larger2_normal()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
 

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

@@ -19,7 +19,6 @@ 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
 
 
 class TensorFillDiagTensor_Test(unittest.TestCase):
@@ -29,7 +28,8 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
         if fluid.core.is_compiled_with_cuda():
             self.places.append(fluid.CUDAPlace(0))
 
-    def func_dim2(self):
+    def test_dim2(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         expected_np = np.array(
             [[1, 2, 2], [2, 1, 2], [2, 2, 1], [2, 2, 2]]
         ).astype('float32')
@@ -59,15 +59,10 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True,
                 )
-
-    def test_dim2(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_dim2()
-        self.func_dim2()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_dim2_offset_1(self):
+    def test_dim2_offset_1(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         expected_np = np.array(
             [[2, 2, 2], [1, 2, 2], [2, 1, 2], [2, 2, 1]]
         ).astype('float32')
@@ -97,15 +92,10 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True,
                 )
-
-    def test_dim2_offset_1(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_dim2_offset_1()
-        self.func_dim2_offset_1()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_dim2_offset1(self):
+    def test_dim2_offset1(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         expected_np = np.array(
             [[2, 1, 2], [2, 2, 1], [2, 2, 2], [2, 2, 2]]
         ).astype('float32')
@@ -135,15 +125,10 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True,
                 )
-
-    def test_dim2_offset1(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_dim2_offset1()
-        self.func_dim2_offset1()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_dim4(self):
+    def test_dim4(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         expected_np = np.array(
             [
                 [
@@ -201,15 +186,10 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True,
                 )
-
-    def test_func_dim4(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_dim4()
-        self.func_dim4()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_largedim(self):
+    def test_largedim(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
         # large dim only test on gpu because the cpu version is too slow for ci test, and the memory is limited
         if len(self.places) > 1:
             bsdim = 1024
@@ -233,12 +213,6 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
 
                 self.assertEqual((y == expected_pred).all(), True)
                 self.assertEqual((y.grad == expected_grad).all(), True)
-
-    def test_largedim(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_largedim()
-        self.func_largedim()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
 

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

@@ -20,7 +20,6 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.nn as nn
-from paddle.fluid.framework import _test_eager_guard
 
 
 class SimpleNet(nn.Layer):
@@ -66,7 +65,9 @@ class TestTensorRegisterHook(unittest.TestCase):
         if paddle.is_compiled_with_cuda():
             self.devices.append("gpu")
 
-    def func_hook_for_interior_var(self):
+    def test_hook_for_interior_var(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
+
         def run_double_hook_for_interior_var(double_hook, removed=False):
             for device in self.devices:
                 paddle.set_device(device)
@@ -155,15 +156,11 @@ class TestTensorRegisterHook(unittest.TestCase):
         run_print_hook_for_interior_var(print_hook)
         # register hook and removed
         run_print_hook_for_interior_var(print_hook, removed=True)
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def test_hook_for_interior_var(self):
+    def test_hook_for_leaf_var(self):
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_hook_for_interior_var()
-        self.func_hook_for_interior_var()
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_hook_for_leaf_var(self):
         def run_double_hook_for_leaf_var(double_hook, removed=False):
             for device in self.devices:
                 paddle.set_device(device)
@@ -201,15 +198,11 @@ class TestTensorRegisterHook(unittest.TestCase):
         run_double_hook_for_leaf_var(lambda grad: grad * 2)
         # register hook and removed
         run_double_hook_for_leaf_var(lambda grad: grad * 2, removed=True)
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def test_hook_for_leaf_var(self):
+    def test_hook_for_accumulated_grad_interior_var(self):
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_hook_for_leaf_var()
-        self.func_hook_for_leaf_var()
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_hook_for_accumulated_grad_interior_var(self):
         def run_double_hook_for_accumulated_grad_interior_var(
             double_hook, removed=False
         ):
@@ -265,15 +258,11 @@ class TestTensorRegisterHook(unittest.TestCase):
         run_double_hook_for_accumulated_grad_interior_var(
             lambda grad: grad * 2, removed=True
         )
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def test_hook_for_accumulated_grad_interior_var(self):
+    def test_hook_for_accumulated_grad_leaf_var(self):
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_hook_for_accumulated_grad_interior_var()
-        self.func_hook_for_accumulated_grad_interior_var()
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_hook_for_accumulated_grad_leaf_var(self):
         def run_double_hook_for_accumulated_grad_leaf_var(
             double_hook, removed=False
         ):
@@ -315,13 +304,11 @@ class TestTensorRegisterHook(unittest.TestCase):
         run_double_hook_for_accumulated_grad_leaf_var(
             lambda grad: grad * 2, removed=True
         )
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def test_hook_for_accumulated_grad_leaf_var(self):
-        with _test_eager_guard():
-            self.func_hook_for_accumulated_grad_leaf_var()
-        self.func_hook_for_accumulated_grad_leaf_var()
+    def test_hook_in_model(self):
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
 
-    def func_hook_in_model(self):
         def run_double_hook_in_model(
             data, label, hook=None, register=False, remove=False
         ):
@@ -378,15 +365,11 @@ class TestTensorRegisterHook(unittest.TestCase):
         np.testing.assert_array_equal(ret1_grad, ret1_grad_rm)
         np.testing.assert_array_equal(linear1_w_grad, linear1_w_grad_rm)
         np.testing.assert_array_equal(linear1_b_grad, linear1_b_grad_rm)
+        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def test_func_hook_in_model(self):
+    def test_multiple_hooks_for_interior_var(self):
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_hook_in_model()
-        self.func_hook_in_model()
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_multiple_hooks_for_interior_var(self):
         def run_multiple_hooks_for_interior_var(
             device, hooks, remove1=False, remove2=False, remove3=False
         ):
@@ -466,15 +449,9 @@ class TestTensorRegisterHook(unittest.TestCase):
             np.testing.assert_array_equal(w_grad, z)
             np.testing.assert_array_equal(x_grad, z)
             np.testing.assert_array_equal(y_grad, z)
-
-    def test_multiple_hooks_for_interior_var(self):
-        fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
-        with _test_eager_guard():
-            self.func_multiple_hooks_for_interior_var()
-        self.func_multiple_hooks_for_interior_var()
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
-    def func_hook_in_double_grad(self):
+    def test_hook_in_double_grad(self):
         def double_print_hook(grad):
             grad = grad * 2
             print(grad)
@@ -510,12 +487,7 @@ class TestTensorRegisterHook(unittest.TestCase):
             z.backward()
             np.testing.assert_array_equal(x.grad.numpy(), np.array([8.0]))
 
-    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):
+    def test_remove_one_hook_multiple_times(self):
         for device in self.devices:
             paddle.set_device(device)
 
@@ -526,12 +498,7 @@ class TestTensorRegisterHook(unittest.TestCase):
             self.assertTrue(h.remove())
             self.assertFalse(h.remove())
 
-    def test_remove_one_hook_multiple_times(self):
-        with _test_eager_guard():
-            self.func_remove_one_hook_multiple_times()
-        self.func_remove_one_hook_multiple_times()
-
-    def func_register_hook_for_stop_gradient_var(self):
+    def test_register_hook_for_stop_gradient_var(self):
         for device in self.devices:
             paddle.set_device(device)
 
@@ -540,11 +507,6 @@ class TestTensorRegisterHook(unittest.TestCase):
             with self.assertRaises(RuntimeError):
                 x.register_hook(lambda grad: grad * 2)
 
-    def test_register_hook_for_stop_gradient_var(self):
-        with _test_eager_guard():
-            self.func_register_hook_for_stop_gradient_var()
-        self.func_register_hook_for_stop_gradient_var()
-
     def test_register_hook_in_static_mode(self):
         paddle.enable_static()
 
@@ -562,7 +524,7 @@ class TestTensorRegisterHook(unittest.TestCase):
 
         paddle.disable_static()
 
-    def func_register_hook_in_dy2static_mode(self):
+    def test_register_hook_in_dy2static_mode(self):
         net = SimpleNetForStatic(self.in_size, self.out_size)
         jit_net = paddle.jit.to_static(
             net, input_spec=[paddle.static.InputSpec([None, self.in_size])]
@@ -576,11 +538,6 @@ class TestTensorRegisterHook(unittest.TestCase):
         with self.assertRaises(AssertionError):
             out = jit_net(data_t)
 
-    def test_register_hook_in_dy2static_mode(self):
-        with _test_eager_guard():
-            self.func_register_hook_in_dy2static_mode()
-        self.func_register_hook_in_dy2static_mode()
-
 
 HOOK_INIT_VALUE = 10
 HOOK_IS_CALLED = False
@@ -599,7 +556,7 @@ class TestTensorRegisterBackwardHook(unittest.TestCase):
         if paddle.is_compiled_with_cuda():
             self.devices.append("gpu")
 
-    def func_register_backward_hook(self):
+    def test_register_backward_hook(self):
         global HOOK_INIT_VALUE
         global HOOK_IS_CALLED
         for device in self.devices:
@@ -616,35 +573,20 @@ class TestTensorRegisterBackwardHook(unittest.TestCase):
             HOOK_INIT_VALUE = 10
             HOOK_IS_CALLED = False
 
-    def test_register_backward_hook(self):
-        with _test_eager_guard():
-            self.func_register_backward_hook()
-        self.func_register_backward_hook()
-
-    def func_register_backward_hook_for_interior_var(self):
+    def test_register_backward_hook_for_interior_var(self):
         x = paddle.to_tensor(5.0, stop_gradient=False)
         y = paddle.pow(x, 4.0)
 
         with self.assertRaises(ValueError):
             y._register_backward_hook(global_void_hook)
 
-    def test_register_backward_hook_for_interior_var(self):
-        with _test_eager_guard():
-            self.func_register_backward_hook_for_interior_var()
-        self.func_register_backward_hook_for_interior_var()
-
-    def func_register_backward_hook_for_var_without_gradient(self):
+    def test_register_backward_hook_for_var_without_gradient(self):
         x = paddle.to_tensor(5.0)
         y = paddle.pow(x, 4.0)
 
         with self.assertRaises(ValueError):
             x._register_backward_hook(global_void_hook)
 
-    def test_register_backward_hook_for_var_without_gradient(self):
-        with _test_eager_guard():
-            self.func_register_backward_hook_for_var_without_gradient()
-        self.func_register_backward_hook_for_var_without_gradient()
-
 
 class TestRegsiterBackwardFinalHook(unittest.TestCase):
     def setUp(self):

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

@@ -17,7 +17,6 @@ import unittest
 import numpy as np
 
 import paddle
-from paddle.fluid.framework import _test_eager_guard
 
 # Support types are ref from `paddle.tensor.math`
 # - Related paddle dtypes:
@@ -50,7 +49,7 @@ class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
         self.assertEqual(c_rlt.dtype, c.dtype)
         np.testing.assert_array_equal(c_rlt.numpy(), c.numpy())
 
-    def func_tensor_add_scalar(self):
+    def test_tensor_add_scalar(self):
         # tensor(int64) + scalar(int)
         a = paddle.ones([2, 2, 2], dtype='int64')
         b = 1
@@ -81,12 +80,7 @@ class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
         c = paddle.full([2, 2, 2], 2.5, dtype="float32")
         self.check_operation(a, b, c, '+')
 
-    def test_tensor_add_scalar(self):
-        with _test_eager_guard():
-            self.func_tensor_add_scalar()
-        self.func_tensor_add_scalar()
-
-    def func_tensor_sub_scalar(self):
+    def test_tensor_sub_scalar(self):
         # tensor(int64) - scalar(int)
         a = paddle.ones([2, 2, 2], dtype='int64')
         b = 1
@@ -117,12 +111,7 @@ class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
         c = paddle.full([2, 2, 2], 0.5, dtype="float32")
         self.check_operation(a, b, c, '-')
 
-    def test_tensor_sub_scalar(self):
-        with _test_eager_guard():
-            self.func_tensor_sub_scalar()
-        self.func_tensor_sub_scalar()
-
-    def func_scalar_sub_tensor(self):
+    def test_scalar_sub_tensor(self):
         # scalar(int) - tensor(int64)
         a = 1
         b = paddle.ones([2, 2, 2], dtype='int64')
@@ -153,12 +142,7 @@ class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
         c = paddle.full([2, 2, 2], -0.5, dtype="float32")
         self.check_operation(a, b, c, '-')
 
-    def test_scalar_sub_tensor(self):
-        with _test_eager_guard():
-            self.func_scalar_sub_tensor()
-        self.func_scalar_sub_tensor()
-
-    def func_tensor_mul_tensor(self):
+    def test_tensor_mul_tensor(self):
         # tensor(int64) * scalar(int)
         a = paddle.ones([2, 2, 2], dtype='int64')
         b = 1
@@ -189,12 +173,7 @@ class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
         c = paddle.full([2, 2, 2], 1.5, dtype="float32")
         self.check_operation(a, b, c, '*')
 
-    def test_tensor_mul_tensor(self):
-        with _test_eager_guard():
-            self.func_tensor_mul_tensor()
-        self.func_tensor_mul_tensor()
-
-    def func_tensor_div_scalar(self):
+    def test_tensor_div_scalar(self):
         # tensor(int64) / scalar(int)
         a = paddle.ones([2, 2, 2], dtype='int64')
         b = 2
@@ -225,12 +204,7 @@ class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
         c = paddle.full([2, 2, 2], 2, dtype="float32")
         self.check_operation(a, b, c, '/')
 
-    def test_tensor_div_scalar(self):
-        with _test_eager_guard():
-            self.func_tensor_div_scalar()
-        self.func_tensor_div_scalar()
-
-    def func_scalar_div_tensor(self):
+    def test_scalar_div_tensor(self):
         # scalar(int) / tensor(int64)
         a = 1
         b = paddle.full([2, 2, 2], 2, dtype='int64')
@@ -255,12 +229,7 @@ class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
         c = paddle.full([2, 2, 2], 2, dtype="float32")
         self.check_operation(a, b, c, '/')
 
-    def test_scalar_div_tensor(self):
-        with _test_eager_guard():
-            self.func_scalar_div_tensor()
-        self.func_scalar_div_tensor()
-
-    def func_tensor_pow_scalar(self):
+    def test_tensor_pow_scalar(self):
         # tensor(int64) ** scalar(int)
         a = paddle.full([2, 2, 2], 2, dtype='int64')
         b = 3
@@ -285,12 +254,7 @@ class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
         c = paddle.full([2, 2, 2], 8, dtype="float32")
         self.check_operation(a, b, c, '**')
 
-    def test_tensor_pow_scalar(self):
-        with _test_eager_guard():
-            self.func_tensor_pow_scalar()
-        self.func_tensor_pow_scalar()
-
-    def func_scalar_pow_tensor(self):
+    def test_scalar_pow_tensor(self):
         # scalar(int) ** tensor(int64)
         a = 3
         b = paddle.full([2, 2, 2], 2, dtype='int64')
@@ -315,25 +279,15 @@ class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
         c = paddle.full([2, 2, 2], 9, dtype="float32")
         self.check_operation(a, b, c, '**')
 
-    def test_scalar_pow_tensor(self):
-        with _test_eager_guard():
-            self.func_scalar_pow_tensor()
-        self.func_scalar_pow_tensor()
-
     # TODO: floordiv op kernel doesn't support float
-    def func_tensor_floordiv_scalar(self):
+    def test_tensor_floordiv_scalar(self):
         # tensor(int64) // scalar(int)
         a = paddle.full([2, 2, 2], 3, dtype='int64')
         b = 2
         c = paddle.full([2, 2, 2], 1, dtype="int64")
         self.check_operation(a, b, c, '//')
 
-    def test_tensor_floordiv_scalar(self):
-        with _test_eager_guard():
-            self.func_tensor_floordiv_scalar()
-        self.func_tensor_floordiv_scalar()
-
-    def func_tensor_mod_scalar(self):
+    def test_tensor_mod_scalar(self):
         # tensor(int64) % scalar(int)
         a = paddle.full([2, 2, 2], 3, dtype='int64')
         b = 2
@@ -358,11 +312,6 @@ class TestTensorScalarTypePromotionDynamic(unittest.TestCase):
         c = paddle.full([2, 2, 2], 1, dtype="float32")
         self.check_operation(a, b, c, '%')
 
-    def test_tensor_mod_scalar(self):
-        with _test_eager_guard():
-            self.func_tensor_mod_scalar()
-        self.func_tensor_mod_scalar()
-
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -16,7 +16,6 @@ import unittest
 import warnings
 
 import paddle
-from paddle.fluid.framework import _test_eager_guard
 
 
 class TestTensorTypePromotion(unittest.TestCase):
@@ -28,42 +27,23 @@ class TestTensorTypePromotion(unittest.TestCase):
         with warnings.catch_warnings(record=True) as context:
             warnings.simplefilter("always")
             self.x + self.y
-            self.assertTrue(
-                "The dtype of left and right variables are not the same"
-                in str(context[-1].message)
-            )
 
     def sub_operator(self):
         with warnings.catch_warnings(record=True) as context:
             warnings.simplefilter("always")
             self.x - self.y
-            self.assertTrue(
-                "The dtype of left and right variables are not the same"
-                in str(context[-1].message)
-            )
 
     def mul_operator(self):
         with warnings.catch_warnings(record=True) as context:
             warnings.simplefilter("always")
             self.x * self.y
-            self.assertTrue(
-                "The dtype of left and right variables are not the same"
-                in str(context[-1].message)
-            )
 
     def div_operator(self):
         with warnings.catch_warnings(record=True) as context:
             warnings.simplefilter("always")
             self.x / self.y
-            self.assertTrue(
-                "The dtype of left and right variables are not the same"
-                in str(context[-1].message)
-            )
 
     def test_operator(self):
-        with _test_eager_guard():
-            pass
-            # add / sub / mul / div has been sunk to cpp level, there is no warnings to catch by this test.
         self.setUp()
         self.add_operator()
         self.sub_operator()

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

@@ -18,11 +18,11 @@ import numpy as np
 
 import paddle
 from paddle.fluid import core
-from paddle.fluid.framework import _in_legacy_dygraph, _test_eager_guard
+from paddle.fluid.framework import _in_legacy_dygraph
 
 
 class TestTensorCopyFrom(unittest.TestCase):
-    def func_main(self):
+    def test_main(self):
         if paddle.fluid.core.is_compiled_with_cuda():
             place = paddle.CPUPlace()
             np_value = np.random.random(size=[10, 30]).astype('float32')
@@ -30,14 +30,9 @@ class TestTensorCopyFrom(unittest.TestCase):
             tensor._uva()
             self.assertTrue(tensor.place.is_gpu_place())
 
-    def test_main(self):
-        with _test_eager_guard():
-            self.func_main()
-        self.func_main()
-
 
 class TestUVATensorFromNumpy(unittest.TestCase):
-    def func_uva_tensor_creation(self):
+    def test_uva_tensor_creation(self):
         if paddle.fluid.core.is_compiled_with_cuda():
             dtype_list = [
                 "int32",
@@ -74,11 +69,6 @@ class TestUVATensorFromNumpy(unittest.TestCase):
                 tensor1.numpy(), tensor2.numpy(), rtol=1e-05
             )
 
-    def test_uva_tensor_creation(self):
-        with _test_eager_guard():
-            self.func_uva_tensor_creation()
-        self.func_uva_tensor_creation()
-
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -18,14 +18,13 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid.framework import _test_eager_guard
 
 
 class TensorFill_Test(unittest.TestCase):
     def setUp(self):
         self.shape = [32, 32]
 
-    def func_test_tensor_fill_true(self):
+    def test_tensor_fill_true(self):
         typelist = ['float32', 'float64', 'int32', 'int64', 'float16']
         places = [fluid.CPUPlace()]
         if fluid.core.is_compiled_with_cuda():
@@ -44,11 +43,6 @@ class TensorFill_Test(unittest.TestCase):
                 tensor.zero_()
                 self.assertEqual((tensor.numpy() == target).all().item(), True)
 
-    def test_tensor_fill_true(self):
-        with _test_eager_guard():
-            self.func_test_tensor_fill_true()
-        self.func_test_tensor_fill_true()
-
 
 if __name__ == '__main__':
     unittest.main()