rm unittests eager guard tests part17 number2pool1d (#48840)

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

@@ -20,7 +20,6 @@ import op_test
 import paddle
 import paddle.fluid.core as core
 from paddle.distributed.models.moe import utils
-from paddle.fluid.framework import _test_eager_guard
 
 
 def count(x, upper_num):
@@ -68,17 +67,12 @@ class TestNumberCountAPI(unittest.TestCase):
             res = exe.run(feed={'x': self.x}, fetch_list=[out])
             assert np.allclose(res, self.out)
 
-    def func_api_dygraph(self):
+    def test_api_dygraph(self):
         paddle.disable_static()
         x = paddle.to_tensor(self.x)
         out = utils._number_count(x, self.upper_num)
         assert np.allclose(out.numpy(), self.out)
 
-    def test_api_dygraph(self):
-        with _test_eager_guard():
-            self.func_api_dygraph()
-        self.func_api_dygraph()
-
 
 if __name__ == '__main__':
     paddle.enable_static()

python/paddle/fluid/tests/unittests/test_one_hot_v2_op.py

@@ -20,7 +20,7 @@ from op_test import OpTest
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
-from paddle.fluid.framework import Program, _test_eager_guard, program_guard
+from paddle.fluid.framework import Program, program_guard
 
 
 class TestOneHotOp(OpTest):
@@ -182,10 +182,9 @@ class TestOneHotOpApi(unittest.TestCase):
             one_hot_label = paddle.nn.functional.one_hot(
                 fluid.dygraph.to_variable(label), depth
             )
-            with _test_eager_guard():
-                one_hot_label = paddle.nn.functional.one_hot(
-                    paddle.to_tensor(label), depth
-                )
+            one_hot_label = paddle.nn.functional.one_hot(
+                paddle.to_tensor(label), depth
+            )
 
     def _run(self, depth):
         label = fluid.layers.data(name="label", shape=[1], dtype="int64")

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

@@ -17,7 +17,6 @@ import unittest
 import numpy as np
 
 import paddle
-from paddle.fluid.framework import _test_eager_guard
 
 
 class LinearNet(paddle.nn.Layer):
@@ -41,33 +40,23 @@ class Logic(paddle.nn.Layer):
 
 
 class TestExportWithTensor(unittest.TestCase):
-    def func_with_tensor(self):
+    def test_with_tensor(self):
         self.x_spec = paddle.static.InputSpec(
             shape=[None, 128], dtype='float32'
         )
         model = LinearNet()
         paddle.onnx.export(model, 'linear_net', input_spec=[self.x_spec])
 
-    def test_with_tensor(self):
-        with _test_eager_guard():
-            self.func_with_tensor()
-        self.func_with_tensor()
-
 
 class TestExportWithTensor1(unittest.TestCase):
-    def func_with_tensor(self):
+    def test_with_tensor(self):
         self.x = paddle.to_tensor(np.random.random((1, 128)))
         model = LinearNet()
         paddle.onnx.export(model, 'linear_net', input_spec=[self.x])
 
-    def test_with_tensor(self):
-        with _test_eager_guard():
-            self.func_with_tensor()
-        self.func_with_tensor()
-
 
 class TestExportPrunedGraph(unittest.TestCase):
-    def func_prune_graph(self):
+    def test_prune_graph(self):
         model = Logic()
         self.x = paddle.to_tensor(np.array([1]))
         self.y = paddle.to_tensor(np.array([-1]))
@@ -77,12 +66,6 @@ class TestExportPrunedGraph(unittest.TestCase):
             model, 'pruned', input_spec=[self.x], output_spec=[out]
         )
 
-    def test_prune_graph(self):
-        # test eager
-        with _test_eager_guard():
-            self.func_prune_graph()
-        self.func_prune_graph()
-
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -27,7 +27,6 @@ import paddle.fluid.optimizer as optimizer
 from paddle.fluid.backward import append_backward
 from paddle.fluid.framework import (
     Program,
-    _test_eager_guard,
     convert_np_dtype_to_dtype_,
     program_guard,
 )
@@ -1387,11 +1386,6 @@ class TestOptimizerDtype(unittest.TestCase):
     def test_float32(self):
         self.check_with_dtype('float32')
 
-    def test_api_eager_dygraph(self):
-        with _test_eager_guard():
-            self.test_float64()
-            self.test_float32()
-
 
 class TestMasterWeightSaveForFP16(unittest.TestCase):
     '''

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

@@ -18,7 +18,7 @@ import numpy as np
 
 import paddle
 import paddle.optimizer as optimizer
-from paddle.fluid.framework import _in_legacy_dygraph, _test_eager_guard
+from paddle.fluid.framework import _in_legacy_dygraph
 
 
 class TestOptimizerForVarBase(unittest.TestCase):
@@ -59,71 +59,36 @@ class TestOptimizerForVarBase(unittest.TestCase):
             x.numpy(), np.full([2, 3], -self.lr), rtol=1e-05
         )
 
-    def func_test_adam_with_varbase_list_input(self):
+    def test_adam_with_varbase_list_input(self):
         self.run_optimizer_step_with_varbase_list_input(optimizer.Adam)
         self.run_optimizer_minimize_with_varbase_list_input(optimizer.Adam)
 
-    def test_adam_with_varbase_list_input(self):
-        with _test_eager_guard():
-            self.func_test_adam_with_varbase_list_input()
-        self.func_test_adam_with_varbase_list_input()
-
-    def func_test_sgd_with_varbase_list_input(self):
+    def test_sgd_with_varbase_list_input(self):
         self.run_optimizer_step_with_varbase_list_input(optimizer.SGD)
         self.run_optimizer_minimize_with_varbase_list_input(optimizer.SGD)
 
-    def test_sgd_with_varbase_list_input(self):
-        with _test_eager_guard():
-            self.func_test_sgd_with_varbase_list_input()
-        self.func_test_sgd_with_varbase_list_input()
-
-    def func_test_adagrad_with_varbase_list_input(self):
+    def test_adagrad_with_varbase_list_input(self):
         self.run_optimizer_step_with_varbase_list_input(optimizer.Adagrad)
         self.run_optimizer_minimize_with_varbase_list_input(optimizer.Adagrad)
 
-    def test_adagrad_with_varbase_list_input(self):
-        with _test_eager_guard():
-            self.func_test_adagrad_with_varbase_list_input()
-        self.func_test_adagrad_with_varbase_list_input()
-
-    def func_test_adamw_with_varbase_list_input(self):
+    def test_adamw_with_varbase_list_input(self):
         self.run_optimizer_step_with_varbase_list_input(optimizer.AdamW)
         self.run_optimizer_minimize_with_varbase_list_input(optimizer.AdamW)
 
-    def test_adamw_with_varbase_list_input(self):
-        with _test_eager_guard():
-            self.func_test_adamw_with_varbase_list_input()
-        self.func_test_adamw_with_varbase_list_input()
-
-    def func_test_adamax_with_varbase_list_input(self):
+    def test_adamax_with_varbase_list_input(self):
         self.run_optimizer_step_with_varbase_list_input(optimizer.Adamax)
         self.run_optimizer_minimize_with_varbase_list_input(optimizer.Adamax)
 
-    def test_adamax_with_varbase_list_input(self):
-        with _test_eager_guard():
-            self.func_test_adamax_with_varbase_list_input()
-        self.func_test_adamax_with_varbase_list_input()
-
-    def func_test_momentum_with_varbase_list_input(self):
+    def test_momentum_with_varbase_list_input(self):
         self.run_optimizer_step_with_varbase_list_input(optimizer.Momentum)
         self.run_optimizer_minimize_with_varbase_list_input(optimizer.Momentum)
 
-    def test_momentum_with_varbase_list_input(self):
-        with _test_eager_guard():
-            self.func_test_momentum_with_varbase_list_input()
-        self.func_test_momentum_with_varbase_list_input()
-
-    def func_test_optimizer_with_varbase_input(self):
+    def test_optimizer_with_varbase_input(self):
         x = paddle.zeros([2, 3])
         with self.assertRaises(TypeError):
             optimizer.Adam(learning_rate=self.lr, parameters=x)
 
-    def test_optimizer_with_varbase_input(self):
-        with _test_eager_guard():
-            self.func_test_optimizer_with_varbase_input()
-        self.func_test_optimizer_with_varbase_input()
-
-    def func_test_create_param_lr_with_1_for_coverage(self):
+    def test_create_param_lr_with_1_for_coverage(self):
         if _in_legacy_dygraph():
             x = paddle.fluid.framework.ParamBase(
                 dtype="float32",
@@ -151,12 +116,7 @@ class TestOptimizerForVarBase(unittest.TestCase):
         z.backward()
         opt.step()
 
-    def test_create_param_lr_with_1_for_coverage(self):
-        with _test_eager_guard():
-            self.func_test_create_param_lr_with_1_for_coverage()
-        self.func_test_create_param_lr_with_1_for_coverage()
-
-    def func_test_create_param_lr_with_no_1_value_for_coverage(self):
+    def test_create_param_lr_with_no_1_value_for_coverage(self):
         if _in_legacy_dygraph():
             x = paddle.fluid.framework.ParamBase(
                 dtype="float32",
@@ -184,11 +144,6 @@ class TestOptimizerForVarBase(unittest.TestCase):
         z.backward()
         opt.step()
 
-    def test_create_param_lr_with_no_1_value_for_coverage(self):
-        with _test_eager_guard():
-            self.func_test_create_param_lr_with_1_for_coverage()
-        self.func_test_create_param_lr_with_1_for_coverage()
-
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -17,7 +17,6 @@ import unittest
 import numpy as np
 
 import paddle
-from paddle.fluid.framework import _test_eager_guard
 from paddle.static import Program, program_guard
 
 
@@ -54,7 +53,7 @@ class TestMultiplyApi(unittest.TestCase):
         res = paddle.outer(x, y)
         return res.numpy()
 
-    def func_test_multiply(self):
+    def test_multiply(self):
         np.random.seed(7)
 
         # test static computation graph: 3-d array
@@ -113,14 +112,9 @@ class TestMultiplyApi(unittest.TestCase):
         res = self._run_dynamic_graph_case(x_data, y_data)
         np.testing.assert_allclose(res, np.outer(x_data, y_data), rtol=1e-05)
 
-    def test_multiply(self):
-        with _test_eager_guard():
-            self.func_test_multiply()
-        self.func_test_multiply()
-
 
 class TestMultiplyError(unittest.TestCase):
-    def func_test_errors(self):
+    def test_errors(self):
         # test static computation graph: dtype can not be int8
         paddle.enable_static()
         with program_guard(Program(), Program()):
@@ -161,11 +155,6 @@ class TestMultiplyError(unittest.TestCase):
         y_data = np.random.randn(200).astype(np.float32)
         self.assertRaises(ValueError, paddle.outer, x_data, y_data)
 
-    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_paddle_imperative_double_grad.py

@@ -19,7 +19,7 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid.framework import _in_legacy_dygraph, _test_eager_guard
+from paddle.fluid.framework import _in_legacy_dygraph
 from paddle.fluid.wrapped_decorator import wrap_decorator
 
 
@@ -68,7 +68,7 @@ class TestDygraphDoubleGrad(TestCase):
         )
 
     @dygraph_guard
-    def func_exception(self):
+    def test_exception(self):
         with self.assertRaises(AssertionError):
             self.grad(None, None)
 
@@ -101,13 +101,8 @@ 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
@@ -141,13 +136,8 @@ 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_none_one_initial_gradient(self):
+    def test_none_one_initial_gradient(self):
         numel = 1
         for s in self.shape:
             numel *= s
@@ -223,11 +213,6 @@ 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):
         x = random_var(self.shape)
@@ -269,13 +254,8 @@ 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
@@ -321,13 +301,8 @@ class TestDygraphDoubleGrad(TestCase):
                 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
@@ -363,11 +338,6 @@ class TestDygraphDoubleGrad(TestCase):
                 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):

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

@@ -22,12 +22,7 @@ import paddle.fluid.core as core
 import paddle.fluid.io as io
 from paddle.fluid.dygraph import guard
 from paddle.fluid.executor import Executor
-from paddle.fluid.framework import (
-    ParamBase,
-    Variable,
-    _test_eager_guard,
-    default_main_program,
-)
+from paddle.fluid.framework import ParamBase, Variable, default_main_program
 from paddle.fluid.initializer import ConstantInitializer
 
 paddle.enable_static()
@@ -59,7 +54,7 @@ class ParameterChecks(unittest.TestCase):
         zero_dim_param = b.create_parameter(name='x', shape=[], dtype='float32')
         self.assertEqual(zero_dim_param.shape, ())
 
-    def func_parambase(self):
+    def test_parambase(self):
         with guard():
             linear = paddle.nn.Linear(10, 10)
             param = linear.weight
@@ -85,11 +80,6 @@ class ParameterChecks(unittest.TestCase):
             zero_dim_param = ParamBase(shape=[], dtype='float32')
             self.assertEqual(zero_dim_param.shape, [])
 
-    def test_parambase(self):
-        with _test_eager_guard():
-            self.func_parambase()
-        self.func_parambase()
-
     def func_exception(self):
         b = main_program.global_block()
         with self.assertRaises(ValueError):
@@ -109,7 +99,7 @@ class ParameterChecks(unittest.TestCase):
                 name='test', shape=[-1], dtype='float32', initializer=None
             )
 
-    def func_parambase_to_vector(self):
+    def test_parambase_to_vector(self):
         with guard():
             initializer = paddle.ParamAttr(
                 initializer=paddle.nn.initializer.Constant(3.0)
@@ -135,11 +125,6 @@ class ParameterChecks(unittest.TestCase):
             self.assertTrue(linear2.weight.is_leaf, True)
             self.assertTrue(linear2.bias.is_leaf, True)
 
-    def test_parambase_to_vector(self):
-        with _test_eager_guard():
-            self.func_parambase_to_vector()
-        self.func_parambase_to_vector()
-
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -19,7 +19,6 @@ import numpy as np
 from op_test import OpTest
 
 import paddle
-from paddle.fluid.framework import _test_eager_guard
 
 paddle.enable_static()
 paddle.seed(100)
@@ -103,13 +102,12 @@ class TestPoissonAPI(unittest.TestCase):
             y = paddle.poisson(x)
             self.assertTrue(np.min(y.numpy()) >= 0)
 
-            with _test_eager_guard():
-                x = paddle.randn([10, 10], dtype='float32')
-                x.stop_gradient = False
-                y = paddle.poisson(x)
-                y.backward()
-                self.assertTrue(np.min(y.numpy()) >= 0)
-                np.testing.assert_array_equal(np.zeros_like(x), x.gradient())
+            x = paddle.randn([10, 10], dtype='float32')
+            x.stop_gradient = False
+            y = paddle.poisson(x)
+            y.backward()
+            self.assertTrue(np.min(y.numpy()) >= 0)
+            np.testing.assert_array_equal(np.zeros_like(x), x.gradient())
 
     def test_fixed_random_number(self):
         # Test GPU Fixed random number, which is generated by 'curandStatePhilox4_32_10_t'

python/paddle/fluid/tests/unittests/test_pool1d_api.py

@@ -20,7 +20,6 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.nn.functional as F
-from paddle.fluid.framework import _test_eager_guard
 
 
 def adaptive_start_index(index, input_size, output_size):
@@ -274,10 +273,6 @@ class TestPool1D_API(unittest.TestCase):
             self.check_avg_dygraph_padding_same(place)
             self.check_max_dygraph_return_index_results(place)
 
-    def test_dygraph_api(self):
-        with _test_eager_guard():
-            self.test_pool1d()
-
 
 class TestPool2DError_API(unittest.TestCase):
     def test_error_api(self):
@@ -422,10 +417,6 @@ class TestPool2DError_API(unittest.TestCase):
 
         self.assertRaises(ValueError, run_stride_out_of_range)
 
-    def test_dygraph_api(self):
-        with _test_eager_guard():
-            self.test_error_api()
-
 
 if __name__ == '__main__':
     unittest.main()