Yj/rm imperative dygraph eager tests (#48782)

* rm imperative op eager guard tests

* modify varbase patch eager guard doc

python/paddle/fluid/contrib/slim/tests/test_imperative_out_scale.py

@@ -25,7 +25,7 @@ import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 from paddle.fluid import core
 from paddle.fluid.optimizer import AdamOptimizer
-from paddle.fluid.framework import IrGraph, _test_eager_guard
+from paddle.fluid.framework import IrGraph
 from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
 from paddle.nn import Sequential
 from paddle.jit.translated_layer import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
@@ -139,7 +139,7 @@ class TestImperativeOutSclae(unittest.TestCase):
     def tearDown(self):
         self.root_path.cleanup()
 
-    def func_out_scale_acc(self):
+    def test_out_scale_acc(self):
         seed = 1000
         lr = 0.001
 
@@ -208,11 +208,6 @@ class TestImperativeOutSclae(unittest.TestCase):
                 msg='Failed to do the imperative qat.',
             )
 
-    def test_out_scale_acc(self):
-        with _test_eager_guard():
-            self.func_out_scale_acc()
-        self.func_out_scale_acc()
-
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/contrib/slim/tests/test_imperative_ptq.py

@@ -28,7 +28,6 @@ import paddle.fluid as fluid
 from paddle.fluid.contrib.slim.quantization import *
 from paddle.fluid.log_helper import get_logger
 from paddle.dataset.common import download
-from paddle.fluid.framework import _test_eager_guard
 
 from imperative_test_utils import (
     fix_model_dict,
@@ -208,7 +207,7 @@ class TestImperativePTQ(unittest.TestCase):
                 break
         return top1_correct_num / total_num
 
-    def func_ptq(self):
+    def test_ptq(self):
         start_time = time.time()
 
         self.set_vars()
@@ -266,14 +265,9 @@ class TestImperativePTQ(unittest.TestCase):
             end_time = time.time()
             print("total time: %ss \n" % (end_time - start_time))
 
-    def test_ptq(self):
-        with _test_eager_guard():
-            self.func_ptq()
-        self.func_ptq()
-
 
 class TestImperativePTQfuse(TestImperativePTQ):
-    def func_ptq(self):
+    def test_ptq(self):
         start_time = time.time()
 
         self.set_vars()
@@ -342,11 +336,6 @@ class TestImperativePTQfuse(TestImperativePTQ):
             end_time = time.time()
             print("total time: %ss \n" % (end_time - start_time))
 
-    def test_ptq(self):
-        with _test_eager_guard():
-            self.func_ptq()
-        self.func_ptq()
-
 
 class TestImperativePTQHist(TestImperativePTQ):
     def set_vars(self):

python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py

@@ -33,7 +33,6 @@ from paddle.nn.quant.quant_layers import (
     QuantizedConv2D,
     QuantizedConv2DTranspose,
 )
-from paddle.fluid.framework import _test_eager_guard
 from imperative_test_utils import fix_model_dict, ImperativeLenet
 
 paddle.enable_static()
@@ -63,7 +62,7 @@ class TestImperativeQat(unittest.TestCase):
         self.diff_threshold = 0.03125
         self.fuse_conv_bn = False
 
-    def func_qat(self):
+    def test_qat(self):
         self.set_vars()
 
         imperative_qat = ImperativeQuantAware(
@@ -244,11 +243,6 @@ class TestImperativeQat(unittest.TestCase):
             delta_value = fp32_acc - quant_acc
             self.assertLessEqual(delta_value, self.diff_threshold)
 
-    def test_qat(self):
-        with _test_eager_guard():
-            self.func_qat()
-        self.func_qat()
-
 
 class TestImperativeQatONNXFormat(unittest.TestCase):
     def set_vars(self):

python/paddle/fluid/contrib/slim/tests/test_imperative_qat_amp.py

@@ -26,7 +26,6 @@ import paddle.fluid as fluid
 from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
 from paddle.fluid.log_helper import get_logger
 from paddle.dataset.common import download
-from paddle.fluid.framework import _test_eager_guard
 from imperative_test_utils import fix_model_dict, ImperativeLenet
 
 os.environ["CPU_NUM"] = "1"
@@ -188,7 +187,7 @@ class TestImperativeQatAmp(unittest.TestCase):
         acc_top1 = sum(acc_top1_list) / len(acc_top1_list)
         return acc_top1
 
-    def ptq(self):
+    def test_ptq(self):
         start_time = time.time()
 
         self.set_vars()
@@ -239,11 +238,6 @@ class TestImperativeQatAmp(unittest.TestCase):
         end_time = time.time()
         print("total time: %ss" % (end_time - start_time))
 
-    def test_ptq(self):
-        self.ptq()
-        with _test_eager_guard():
-            self.ptq()
-
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/contrib/slim/tests/test_imperative_qat_lsq.py

@@ -38,7 +38,6 @@ from paddle.nn.quant.quant_layers import (
     QuantizedConv2D,
     QuantizedConv2DTranspose,
 )
-from paddle.fluid.framework import _test_eager_guard
 from imperative_test_utils import fix_model_dict
 
 paddle.enable_static()

python/paddle/fluid/contrib/slim/tests/test_imperative_qat_user_defined.py

@@ -26,7 +26,6 @@ from paddle.nn import Sequential
 from paddle.nn import Linear
 from paddle.nn.quant.quant_layers import QuantizedConv2DTranspose
 from paddle.fluid.log_helper import get_logger
-from paddle.fluid.framework import _test_eager_guard
 
 os.environ["CPU_NUM"] = "1"
 
@@ -161,7 +160,7 @@ class TestUserDefinedActPreprocess(unittest.TestCase):
         _logger.info("test act_preprocess")
         self.imperative_qat = ImperativeQuantAware(act_preprocess_layer=PACT)
 
-    def func_quant_aware_training(self):
+    def test_quant_aware_training(self):
         imperative_qat = self.imperative_qat
         seed = 1
         np.random.seed(seed)
@@ -263,11 +262,6 @@ class TestUserDefinedActPreprocess(unittest.TestCase):
         train(lenet)
         test(lenet)
 
-    def test_quant_aware_training(self):
-        with _test_eager_guard():
-            self.func_quant_aware_training()
-        self.func_quant_aware_training()
-
 
 class TestUserDefinedWeightPreprocess(TestUserDefinedActPreprocess):
     def setUp(self):

python/paddle/fluid/contrib/slim/tests/test_imperative_skip_op.py

@@ -33,7 +33,6 @@ from imperative_test_utils import (
     train_lenet,
     ImperativeLenetWithSkipQuant,
 )
-from paddle.fluid.framework import _test_eager_guard
 
 os.environ["CPU_NUM"] = "1"
 if core.is_compiled_with_cuda():
@@ -45,7 +44,7 @@ _logger = get_logger(
 
 
 class TestImperativeOutSclae(unittest.TestCase):
-    def func_out_scale_acc(self):
+    def test_out_scale_acc(self):
         paddle.disable_static()
         seed = 1000
         lr = 0.1
@@ -141,11 +140,6 @@ class TestImperativeOutSclae(unittest.TestCase):
         if find_matmul:
             self.assertTrue(matmul_skip_count == 1)
 
-    def test_out_scale_acc(self):
-        with _test_eager_guard():
-            self.func_out_scale_acc()
-        self.func_out_scale_acc()
-
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/dygraph/varbase_patch_methods.py

@@ -965,14 +965,12 @@ def monkey_patch_varbase():
             .. code-block:: python
 
                 import paddle
-                from paddle.fluid.framework import _test_eager_guard
-                with _test_eager_guard():
-                    indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
-                    values = [1, 2, 3, 4, 5]
-                    dense_shape = [3, 4]
-                    sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int32'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape)
-                    print(sparse_x.values())
-                    #[1, 2, 3, 4, 5]
+                indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
+                values = [1, 2, 3, 4, 5]
+                dense_shape = [3, 4]
+                sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int32'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape)
+                print(sparse_x.values())
+                #[1, 2, 3, 4, 5]
         """
         return _C_ops.sparse_values(self)
 
@@ -990,16 +988,14 @@ def monkey_patch_varbase():
             .. code-block:: python
 
                 import paddle
-                from paddle.fluid.framework import _test_eager_guard
-                with _test_eager_guard():
-                    indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
-                    values = [1, 2, 3, 4, 5]
-                    dense_shape = [3, 4]
-                    sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int64'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape)
-                    dense_x = sparse_x.to_dense()
-                    #[[0., 1., 0., 2.],
-                    # [0., 0., 3., 0.],
-                    # [4., 5., 0., 0.]]
+                indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
+                values = [1, 2, 3, 4, 5]
+                dense_shape = [3, 4]
+                sparse_x = paddle.sparse.sparse_coo_tensor(paddle.to_tensor(indices, dtype='int64'), paddle.to_tensor(values, dtype='float32'), shape=dense_shape)
+                dense_x = sparse_x.to_dense()
+                #[[0., 1., 0., 2.],
+                # [0., 0., 3., 0.],
+                # [4., 5., 0., 0.]]
         """
 
         return _C_ops.sparse_to_dense(self)
@@ -1018,14 +1014,12 @@ def monkey_patch_varbase():
             .. code-block:: python
 
                 import paddle
-                from paddle.fluid.framework import _test_eager_guard
-                with _test_eager_guard():
-                    dense_x = [[0, 1, 0, 2], [0, 0, 3, 4]]
-                    dense_x = paddle.to_tensor(dense_x, dtype='float32')
-                    sparse_x = dense_x.to_sparse_coo(sparse_dim=2)
-                    #indices=[[0, 0, 1, 1],
-                    #         [1, 3, 2, 3]],
-                    #values=[1., 2., 3., 4.]
+                dense_x = [[0, 1, 0, 2], [0, 0, 3, 4]]
+                dense_x = paddle.to_tensor(dense_x, dtype='float32')
+                sparse_x = dense_x.to_sparse_coo(sparse_dim=2)
+                #indices=[[0, 0, 1, 1],
+                #         [1, 3, 2, 3]],
+                #values=[1., 2., 3., 4.]
         """
 
         return _C_ops.sparse_to_sparse_coo(self, sparse_dim)