diff --git a/mindspore/context.py b/mindspore/context.py
index 3c073dd5a66..ba71e4bb47c 100644
--- a/mindspore/context.py
+++ b/mindspore/context.py
@@ -147,7 +147,7 @@ class _Context:
 
     def __init__(self):
         self._thread_local_info = _ThreadLocalInfo()
-        self._context_switches = _ContextSwitchInfo(True)
+        self._context_switches = _ContextSwitchInfo(False)
         self._context_handle = MSContext.get_instance()
 
     def __new__(cls, *args, **kwargs):
@@ -522,7 +522,7 @@ def set_context(**kwargs):
     Context should be configured before running your program. If there is no configuration,
     it will automatic acquisition according to device target by default. GRAPH_MODE or
     PYNATIVE_MODE can be set by `mode` attribute and both modes support all backends, default
-    mode is PYNATIVE_MODE.
+    mode is GRAPH_MODE.
 
     When the `save_graphs` attribute is set to True, attribute of `save_graphs_path` is used to set the
     intermediate compilation graph storage path. By default, the graphs are saved in the current directory.
@@ -532,7 +532,7 @@ def set_context(**kwargs):
     Note:
         Attribute name is required for setting attributes.
         The mode is not recommended to be changed after net was initialized because the implementations of some
-        operations are different in graph mode and pynative mode. Default: PYNATIVE_MODE.
+        operations are different in graph mode and pynative mode. Default: GRAPH_MODE.
 
     Some configurations are device specific, see the below table for details:
 
@@ -555,7 +555,7 @@ def set_context(**kwargs):
     ===========================  ===========================  =================
 
     Args:
-        mode (int): Running in GRAPH_MODE(0) or PYNATIVE_MODE(1). Default: PYNATIVE_MODE(1).
+        mode (int): Running in GRAPH_MODE(0) or PYNATIVE_MODE(1). Default: GRAPH_MODE(0).
         precompile_only (bool): Whether to only precompile the network. If set, the network will only be compiled and
              not executed. Default: False.
         device_target (str): The target device to run, support "Ascend", "GPU", and "CPU".
diff --git a/mindspore/core/utils/ms_context.cc b/mindspore/core/utils/ms_context.cc
index 255c7d1f044..f0a1f787eeb 100644
--- a/mindspore/core/utils/ms_context.cc
+++ b/mindspore/core/utils/ms_context.cc
@@ -61,7 +61,7 @@ MsContext::MsContext(const std::string &policy, const std::string &target) {
 
   set_param<uint32_t>(MS_CTX_MAX_CALL_DEPTH, MAX_CALL_DEPTH_DEFAULT);
   set_param<std::string>(MS_CTX_DEVICE_TARGET, target);
-  set_param<int>(MS_CTX_EXECUTION_MODE, kPynativeMode);
+  set_param<int>(MS_CTX_EXECUTION_MODE, kGraphMode);
   set_param<bool>(MS_CTX_ENABLE_TASK_SINK, true);
   set_param<bool>(MS_CTX_IR_FUSION_FLAG, true);
   set_param<bool>(MS_CTX_ENABLE_HCCL, false);
diff --git a/tests/st/control/test_if_by_if.py b/tests/st/control/test_if_by_if.py
index a1b77c8e528..67917b90cca 100644
--- a/tests/st/control/test_if_by_if.py
+++ b/tests/st/control/test_if_by_if.py
@@ -6,6 +6,7 @@ from mindspore.common.parameter import Parameter
 from mindspore.nn import Cell
 import mindspore.ops.operations as P
 
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 @pytest.mark.level0
@@ -34,7 +35,6 @@ def test_if_by_if_basic():
     class Net(Cell):
         def __init__(self):
             super().__init__()
-            context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
             self.subnet = SubNet()
             self.relu = P.ReLU()
             self.add = P.Add()
diff --git a/tests/st/explainer/test_utils.py b/tests/st/explainer/test_utils.py
index b8e4d8c9e1a..29f59bf324b 100644
--- a/tests/st/explainer/test_utils.py
+++ b/tests/st/explainer/test_utils.py
@@ -19,6 +19,7 @@ import pytest
 
 import mindspore as ms
 import mindspore.nn as nn
+from mindspore import context
 
 from mindspore.explainer._utils import (
     ForwardProbe,
@@ -27,6 +28,7 @@ from mindspore.explainer._utils import (
     retrieve_layer_by_name)
 from mindspore.explainer.explanation._attribution._backprop.backprop_utils import GradNet, get_bp_weights
 
+context.set_context(mode=context.PYNATIVE_MODE)
 
 class CustomNet(nn.Cell):
     """Simple net for test."""
diff --git a/tests/st/export_and_load/test_bgcf.py b/tests/st/export_and_load/test_bgcf.py
index ee80add9a0f..8f9785d0d7c 100644
--- a/tests/st/export_and_load/test_bgcf.py
+++ b/tests/st/export_and_load/test_bgcf.py
@@ -18,12 +18,14 @@ import numpy as np
 import pytest
 
 import mindspore.nn as nn
-from mindspore import Parameter, Tensor
+from mindspore import Parameter, Tensor, context
 from mindspore.ops import operations as P
 from mindspore.common import dtype as mstype
 from mindspore.common.initializer import initializer
 from mindspore.train.serialization import export
 
+context.set_context(mode=context.PYNATIVE_MODE)
+
 
 class MeanConv(nn.Cell):
     def __init__(self,
diff --git a/tests/st/numpy_native/test_array_creations.py b/tests/st/numpy_native/test_array_creations.py
index 05b5784c42c..a221bb8e47b 100644
--- a/tests/st/numpy_native/test_array_creations.py
+++ b/tests/st/numpy_native/test_array_creations.py
@@ -18,10 +18,12 @@ import pytest
 import numpy as onp
 
 import mindspore.numpy as mnp
+from mindspore import context
 
 from .utils import rand_int, rand_bool, match_array, match_res, match_meta, \
     match_all_arrays, run_multi_test, to_tensor
 
+context.set_context(mode=context.PYNATIVE_MODE)
 
 class Cases():
     def __init__(self):
diff --git a/tests/st/numpy_native/test_array_ops.py b/tests/st/numpy_native/test_array_ops.py
index a8ed42c0b6b..1e0081c8ead 100644
--- a/tests/st/numpy_native/test_array_ops.py
+++ b/tests/st/numpy_native/test_array_ops.py
@@ -20,11 +20,14 @@ import pytest
 import numpy as onp
 
 import mindspore.numpy as mnp
+from mindspore import context
 from mindspore.nn import Cell
 
 from .utils import rand_int, run_non_kw_test, check_all_results, match_array, \
     rand_bool, match_res, run_multi_test, to_tensor, match_all_arrays
 
+context.set_context(mode=context.PYNATIVE_MODE)
+
 
 class Cases():
     def __init__(self):
diff --git a/tests/st/numpy_native/test_logic_ops.py b/tests/st/numpy_native/test_logic_ops.py
index 95b7dc3d8db..d0f48b68aa8 100644
--- a/tests/st/numpy_native/test_logic_ops.py
+++ b/tests/st/numpy_native/test_logic_ops.py
@@ -18,10 +18,13 @@ import pytest
 import numpy as onp
 
 import mindspore.numpy as mnp
+from mindspore import context
 
 from .utils import rand_int, rand_bool, run_binop_test, run_logical_test, match_res, \
     match_all_arrays, to_tensor
 
+context.set_context(mode=context.PYNATIVE_MODE)
+
 
 class Cases():
     def __init__(self):
diff --git a/tests/st/numpy_native/test_math_ops.py b/tests/st/numpy_native/test_math_ops.py
index 6d76b4dd604..0a5510326d3 100644
--- a/tests/st/numpy_native/test_math_ops.py
+++ b/tests/st/numpy_native/test_math_ops.py
@@ -18,11 +18,14 @@ import pytest
 import numpy as onp
 
 import mindspore.numpy as mnp
+from mindspore import context
 from mindspore.common.dtype import dtype_to_nptype
 
 from .utils import rand_int, rand_bool, run_binop_test, run_unary_test, run_multi_test, \
     run_single_test, match_res, match_array, match_meta, match_all_arrays, to_tensor
 
+context.set_context(mode=context.PYNATIVE_MODE)
+
 class Cases():
     def __init__(self):
         self.arrs = [
diff --git a/tests/st/ops/cpu/test_norm_op.py b/tests/st/ops/cpu/test_norm_op.py
index 7d2bcb5ac85..5eedca71190 100644
--- a/tests/st/ops/cpu/test_norm_op.py
+++ b/tests/st/ops/cpu/test_norm_op.py
@@ -22,7 +22,7 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import ms_function
 
-context.set_context(device_target='CPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='CPU')
 
 class NetNorm(nn.Cell):
     def __init__(self):
diff --git a/tests/st/ops/cpu/test_one_hot_op.py b/tests/st/ops/cpu/test_one_hot_op.py
index fb825179c36..77d5ea3436e 100644
--- a/tests/st/ops/cpu/test_one_hot_op.py
+++ b/tests/st/ops/cpu/test_one_hot_op.py
@@ -21,7 +21,7 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import ms_function
 
-context.set_context(device_target='CPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='CPU')
 
 
 class NetOneHot(nn.Cell):
diff --git a/tests/st/ops/cpu/test_reduce_op.py b/tests/st/ops/cpu/test_reduce_op.py
index 5876dd797d8..2f69d892cb3 100644
--- a/tests/st/ops/cpu/test_reduce_op.py
+++ b/tests/st/ops/cpu/test_reduce_op.py
@@ -21,7 +21,7 @@ import mindspore.nn as nn
 import mindspore.context as context
 from mindspore.common.api import ms_function
 
-context.set_context(device_target="CPU")
+context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
 
 
 class NetReduce(nn.Cell):
diff --git a/tests/st/ops/cpu/test_transpose_op.py b/tests/st/ops/cpu/test_transpose_op.py
index 72ee56f4af2..52af1ef1b07 100644
--- a/tests/st/ops/cpu/test_transpose_op.py
+++ b/tests/st/ops/cpu/test_transpose_op.py
@@ -23,7 +23,7 @@ from mindspore.common.parameter import Parameter
 import mindspore.nn as nn
 import mindspore.context as context
 
-context.set_context(device_target='CPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='CPU')
 
 
 class Transpose(nn.Cell):
diff --git a/tests/st/ops/gpu/test_adaptive_avg_pool2d_op.py b/tests/st/ops/gpu/test_adaptive_avg_pool2d_op.py
index df20f0e8123..542e21cb69c 100644
--- a/tests/st/ops/gpu/test_adaptive_avg_pool2d_op.py
+++ b/tests/st/ops/gpu/test_adaptive_avg_pool2d_op.py
@@ -23,7 +23,7 @@ from mindspore import Tensor, ops
 from mindspore.ops import operations as P
 from mindspore.common.api import ms_function
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class Net(nn.Cell):
diff --git a/tests/st/ops/gpu/test_add_op.py b/tests/st/ops/gpu/test_add_op.py
index 7b876268591..815ea3ff349 100644
--- a/tests/st/ops/gpu/test_add_op.py
+++ b/tests/st/ops/gpu/test_add_op.py
@@ -60,7 +60,7 @@ class AddNet(nn.Cell):
 
 
 def add(nptype):
-    context.set_context(device_target='GPU')
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
     add_net = AddNet(nptype)
     output = add_net()
diff --git a/tests/st/ops/gpu/test_addn_op.py b/tests/st/ops/gpu/test_addn_op.py
index a55053169b7..1e70cdb7bf0 100644
--- a/tests/st/ops/gpu/test_addn_op.py
+++ b/tests/st/ops/gpu/test_addn_op.py
@@ -22,8 +22,6 @@ from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore.ops import operations as P
 
-context.set_context(device_target='GPU')
-
 
 class Net(nn.Cell):
     def __init__(self):
@@ -39,6 +37,7 @@ class Net(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_net():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
     x = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32)
     y = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32)
     z = np.arange(1 * 3 * 3 * 4).reshape(1, 3, 3, 4).astype(np.float32)
diff --git a/tests/st/ops/gpu/test_batchnorm_fold2_op.py b/tests/st/ops/gpu/test_batchnorm_fold2_op.py
index 0ca186e13b8..cd27f07dce8 100644
--- a/tests/st/ops/gpu/test_batchnorm_fold2_op.py
+++ b/tests/st/ops/gpu/test_batchnorm_fold2_op.py
@@ -23,7 +23,7 @@ from mindspore.common.api import ms_function
 from mindspore.ops import operations as P
 from mindspore.ops.operations import _quant_ops as Q
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class Net(nn.Cell):
diff --git a/tests/st/ops/gpu/test_batchnorm_fold_grad_op.py b/tests/st/ops/gpu/test_batchnorm_fold_grad_op.py
index 6091162a8f4..8ec35d7a6ce 100644
--- a/tests/st/ops/gpu/test_batchnorm_fold_grad_op.py
+++ b/tests/st/ops/gpu/test_batchnorm_fold_grad_op.py
@@ -22,7 +22,7 @@ from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore.ops.operations import _quant_ops as Q
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class Net(nn.Cell):
diff --git a/tests/st/ops/gpu/test_batchnorm_fold_op.py b/tests/st/ops/gpu/test_batchnorm_fold_op.py
index 01f81cb2ad3..7225c29c465 100644
--- a/tests/st/ops/gpu/test_batchnorm_fold_op.py
+++ b/tests/st/ops/gpu/test_batchnorm_fold_op.py
@@ -24,7 +24,7 @@ from mindspore.common.initializer import initializer
 from mindspore.common.api import ms_function
 from mindspore.ops.operations import _quant_ops as Q
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class Net(nn.Cell):
diff --git a/tests/st/ops/gpu/test_concatv2_op.py b/tests/st/ops/gpu/test_concatv2_op.py
index 5ae7c7a4984..9eb2f9e9e27 100644
--- a/tests/st/ops/gpu/test_concatv2_op.py
+++ b/tests/st/ops/gpu/test_concatv2_op.py
@@ -41,7 +41,7 @@ class ConcatV32(nn.Cell):
 
 
 def axis32(nptype):
-    context.set_context(device_target='GPU')
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
     cat = ConcatV32(nptype)
     output = cat()
@@ -98,7 +98,7 @@ class ConcatV43(nn.Cell):
 
 
 def axis43(nptype):
-    context.set_context(device_target='GPU')
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
     cat = ConcatV43(nptype)
     output = cat()
@@ -159,6 +159,8 @@ class ConcatV21(nn.Cell):
 
 
 def axis21(nptype):
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
+
     cat = ConcatV21(nptype)
     output = cat()
     expect = np.array([[0., 1., 0., 1., 2.],
@@ -206,8 +208,9 @@ class Concat3INet(nn.Cell):
 
 
 def concat_3i(nptype):
-    cat = Concat3INet()
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
+    cat = Concat3INet()
     x1_np = np.random.randn(32, 4, 224, 224).astype(nptype)
     x2_np = np.random.randn(32, 8, 224, 224).astype(nptype)
     x3_np = np.random.randn(32, 10, 224, 224).astype(nptype)
@@ -250,6 +253,7 @@ def test_concat_3i_uint8():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_concat_3i_bool():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     cat = Concat3INet()
 
     x1_np = np.random.choice([True, False], (32, 4, 224, 224)).astype(np.bool)
@@ -275,8 +279,9 @@ class Concat4INet(nn.Cell):
 
 
 def concat_4i(nptype):
-    cat = Concat4INet()
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
+    cat = Concat4INet()
     x1_np = np.random.randn(32, 4, 224, 224).astype(nptype)
     x2_np = np.random.randn(32, 8, 224, 224).astype(nptype)
     x3_np = np.random.randn(32, 10, 224, 224).astype(nptype)
@@ -321,8 +326,9 @@ def test_concat_4i_uint8():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_concat_4i_bool():
-    cat = Concat4INet()
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
+    cat = Concat4INet()
     x1_np = np.random.choice([True, False], (32, 4, 224, 224)).astype(np.bool)
     x2_np = np.random.choice([True, False], (32, 8, 224, 224)).astype(np.bool)
     x3_np = np.random.choice([True, False], (32, 10, 224, 224)).astype(np.bool)
diff --git a/tests/st/ops/gpu/test_conv2d_backprop_filter_op.py b/tests/st/ops/gpu/test_conv2d_backprop_filter_op.py
index 3d95c47a97f..9171deeb04d 100644
--- a/tests/st/ops/gpu/test_conv2d_backprop_filter_op.py
+++ b/tests/st/ops/gpu/test_conv2d_backprop_filter_op.py
@@ -23,7 +23,7 @@ from mindspore.common.api import ms_function
 from mindspore.ops import operations as P
 from mindspore.ops.operations import _grad_ops as G
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class Conv2dFilter(nn.Cell):
diff --git a/tests/st/ops/gpu/test_conv2d_backprop_input_op.py b/tests/st/ops/gpu/test_conv2d_backprop_input_op.py
index 1b66fb47958..f3cc2a920dd 100644
--- a/tests/st/ops/gpu/test_conv2d_backprop_input_op.py
+++ b/tests/st/ops/gpu/test_conv2d_backprop_input_op.py
@@ -22,7 +22,7 @@ from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore.ops import operations as P
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class Conv2dInput(nn.Cell):
diff --git a/tests/st/ops/gpu/test_correction_mul_grad_op.py b/tests/st/ops/gpu/test_correction_mul_grad_op.py
index ce32b9ebe3f..efcd257ea17 100644
--- a/tests/st/ops/gpu/test_correction_mul_grad_op.py
+++ b/tests/st/ops/gpu/test_correction_mul_grad_op.py
@@ -22,7 +22,7 @@ from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore.ops.operations import _quant_ops as Q
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class Net(nn.Cell):
diff --git a/tests/st/ops/gpu/test_correction_mul_op.py b/tests/st/ops/gpu/test_correction_mul_op.py
index e9216e1b291..4767dee368d 100644
--- a/tests/st/ops/gpu/test_correction_mul_op.py
+++ b/tests/st/ops/gpu/test_correction_mul_op.py
@@ -22,7 +22,7 @@ from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore.ops.operations import _quant_ops as Q
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class Net(nn.Cell):
diff --git a/tests/st/ops/gpu/test_cudnn_inplace_fusion.py b/tests/st/ops/gpu/test_cudnn_inplace_fusion.py
index c04b78797f9..c10f94a88b5 100644
--- a/tests/st/ops/gpu/test_cudnn_inplace_fusion.py
+++ b/tests/st/ops/gpu/test_cudnn_inplace_fusion.py
@@ -65,8 +65,8 @@ def test_inplace_fusion1():
     x2 = Tensor(x2_np.astype(np.float32))
     x3 = Tensor(x3_np.astype(np.float32))
 
-    net = Conv2dBpropInputInplace(w1, w2)
     context.set_context(device_target='GPU', mode=context.GRAPH_MODE)
+    net = Conv2dBpropInputInplace(w1, w2)
     fusion_output = net(x1, x2, x3)
 
     context.set_context(device_target='GPU', mode=context.PYNATIVE_MODE)
diff --git a/tests/st/ops/gpu/test_cumprod_op.py b/tests/st/ops/gpu/test_cumprod_op.py
index 2cd63039703..482f662385c 100644
--- a/tests/st/ops/gpu/test_cumprod_op.py
+++ b/tests/st/ops/gpu/test_cumprod_op.py
@@ -23,7 +23,7 @@ from mindspore.common.api import ms_function
 from mindspore.ops import operations as P
 
 def cum_prod(nptype):
-    context.set_context(device_target='GPU')
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     x0 = np.random.rand(2, 3, 4, 4).astype(nptype)
     axis0 = 3
 
diff --git a/tests/st/ops/gpu/test_cumsum_op.py b/tests/st/ops/gpu/test_cumsum_op.py
index c1c974bbfc7..6d5ee79da19 100644
--- a/tests/st/ops/gpu/test_cumsum_op.py
+++ b/tests/st/ops/gpu/test_cumsum_op.py
@@ -23,7 +23,7 @@ from mindspore.common.api import ms_function
 from mindspore.ops import operations as P
 
 def cum_sum(nptype):
-    context.set_context(device_target='GPU')
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     x0 = np.random.rand(2, 3, 4, 4).astype(nptype)
     axis0 = 3
 
diff --git a/tests/st/ops/gpu/test_dropout.py b/tests/st/ops/gpu/test_dropout.py
index 91382de4bdd..f00a0c091ff 100644
--- a/tests/st/ops/gpu/test_dropout.py
+++ b/tests/st/ops/gpu/test_dropout.py
@@ -34,6 +34,7 @@ class Net(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_dropout():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
     x_shape = [32, 16, 2, 5]
     x = np.ones(x_shape).astype(np.float32)
     keep_prob = 0.4
diff --git a/tests/st/ops/gpu/test_fake_quant_perchannel.py b/tests/st/ops/gpu/test_fake_quant_perchannel.py
index caa7d4b7e80..91185bcc14e 100644
--- a/tests/st/ops/gpu/test_fake_quant_perchannel.py
+++ b/tests/st/ops/gpu/test_fake_quant_perchannel.py
@@ -21,7 +21,7 @@ from mindspore.common.tensor import Tensor
 from mindspore import nn
 from mindspore.ops.operations import _quant_ops as Q
 
-context.set_context(device_target='GPU', device_id=0)
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU', device_id=0)
 
 
 class Net(nn.Cell):
@@ -240,7 +240,7 @@ def test_fake_quant_perchannel10():
     x = np.array([-0.1, 0.0, 0.1, 0.25, 0.5, 0.75,
                   1.0, 1.25, 1.5, 1.75, 2.0, 2.25,
                   63.0, 63.25, 63.5, 63.7, 63.75, 63.8,
-                  63.9, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape(1, 4, 2, 3).astype(np.float32)
+                  63.9, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape((1, 4, 2, 3)).astype(np.float32)
     expect = np.array([0.0, 0.0, 0.0, 0.25, 0.5, 0.75,
                        1.0, 1.25, 1.5, 1.75, 2.0, 2.25,
                        63.0, 63.25, 63.5, 63.75, 63.75, 63.75,
@@ -269,7 +269,7 @@ def test_fake_quant_perchannel11():
     x = np.array([-0.1, 0.0, 0.1, 0.25, 0.5, 0.75,
                   1.0, 1.25, 1.5, 1.75, 2.0, 2.25,
                   63.0, 63.25, 63.3, 63.4, 63.5, 63.6,
-                  63.7, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape(1, 4, 2, 3).astype(np.float32)
+                  63.7, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape((1, 4, 2, 3)).astype(np.float32)
     expect = np.array([0.0, 0.0, 0.0, 0.25, 0.5, 0.75,
                        1.0, 1.25, 1.5, 1.75, 2.0, 2.25,
                        63.0, 63.25, 63.25, 63.5, 63.5, 63.5,
@@ -296,7 +296,7 @@ def test_fake_quant_perchannel12():
     x = np.array([-0.3, -0.25, -0.2, 0.0, 0.25, 0.5,
                   0.75, 1.0, 1.25, 1.5, 1.75, 2.0,
                   63.0, 63.25, 63.4, 63.5, 63.6, 63.7,
-                  100.0, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape(1, 4, 2, 3).astype(np.float32)
+                  100.0, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape((1, 4, 2, 3)).astype(np.float32)
     expect = np.array([-0.25, -0.25, -0.25, 0.0, 0.25, 0.5,
                        0.75, 1.0, 1.25, 1.5, 1.75, 2.0,
                        63.0, 63.25, 63.5, 63.5, 63.5, 63.5,
@@ -325,7 +325,7 @@ def test_fake_quant_perchannel13():
     x = np.array([-0.3, -0.25, -0.2, 0.0, 0.25, 0.5,
                   0.75, 1.0, 1.25, 1.5, 1.75, 2.0,
                   63.0, 63.2, 63.25, 63.3, 63.4, 63.5,
-                  100.0, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape(1, 4, 2, 3).astype(np.float32)
+                  100.0, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape((1, 4, 2, 3)).astype(np.float32)
     expect = np.array([-0.25, -0.25, -0.25, 0.0, 0.25, 0.5,
                        0.75, 1.0, 1.25, 1.5, 1.75, 2.0,
                        63.0, 63.25, 63.25, 63.25, 63.25, 63.25,
@@ -526,7 +526,7 @@ def test_fake_quant_perchannel22():
     x = np.array([-0.1, 0.0, 0.1, 0.5, 1.0, 1.5,
                   1.5, 2.0, 2.5, 3.0, 3.5, 4.0,
                   6.0, 6.5, 7.0, 7.4, 7.5, 7.7,
-                  7.8, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape(1, 4, 2, 3).astype(np.float32)
+                  7.8, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape((1, 4, 2, 3)).astype(np.float32)
     expect = np.array([0.0, 0.0, 0.0, 0.5, 1.0, 1.5,
                        1.5, 2.0, 2.5, 3.0, 3.5, 4.0,
                        6.0, 6.5, 7.0, 7.5, 7.5, 7.5,
@@ -553,7 +553,7 @@ def test_fake_quant_perchannel23():
     x = np.array([-0.1, 0.0, 0.1, 0.5, 1.0, 1.5,
                   1.5, 2.0, 2.5, 3.0, 3.5, 4.0,
                   6.0, 6.5, 6.8, 6.9, 7.0, 7.1,
-                  7.2, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape(1, 4, 2, 3).astype(np.float32)
+                  7.2, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape((1, 4, 2, 3)).astype(np.float32)
     expect = np.array([0.0, 0.0, 0.0, 0.5, 1.0, 1.5,
                        1.5, 2.0, 2.5, 3.0, 3.5, 4.0,
                        6.0, 6.5, 7.0, 7.0, 7.0, 7.0,
@@ -580,7 +580,7 @@ def test_fake_quant_perchannel24():
     x = np.array([-0.6, -0.5, -0.4, 0.0, 0.5, 1.0,
                   1.5, 2.0, 2.5, 3.0, 3.5, 4.0,
                   6.0, 6.5, 6.9, 7.0, 7.1, 7.7,
-                  100.0, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape(1, 4, 2, 3).astype(np.float32)
+                  100.0, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape((1, 4, 2, 3)).astype(np.float32)
     expect = np.array([-0.5, -0.5, -0.5, 0.0, 0.5, 1.0,
                        1.5, 2.0, 2.5, 3.0, 3.5, 4.0,
                        6.0, 6.5, 7.0, 7.0, 7.0, 7.0,
@@ -607,7 +607,7 @@ def test_fake_quant_perchannel25():
     x = np.array([-0.6, -0.5, -0.4, 0.0, 0.5, 1.0,
                   1.5, 2.0, 2.5, 3.0, 3.5, 4.0,
                   5.5, 6.0, 6.4, 6.5, 6.6, 6.7,
-                  100.0, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape(1, 4, 2, 3).astype(np.float32)
+                  100.0, 100.0, 100.0, 100.0, 100.0, 1000.0]).reshape((1, 4, 2, 3)).astype(np.float32)
     expect = np.array([-0.5, -0.5, -0.5, 0.0, 0.5, 1.0,
                        1.5, 2.0, 2.5, 3.0, 3.5, 4.0,
                        5.5, 6.0, 6.5, 6.5, 6.5, 6.5,
diff --git a/tests/st/ops/gpu/test_fake_quant_perchannel_grad.py b/tests/st/ops/gpu/test_fake_quant_perchannel_grad.py
index aeb90b68043..09f483a80c4 100644
--- a/tests/st/ops/gpu/test_fake_quant_perchannel_grad.py
+++ b/tests/st/ops/gpu/test_fake_quant_perchannel_grad.py
@@ -20,7 +20,7 @@ import mindspore.nn as nn
 import mindspore.context as context
 from mindspore.ops.operations import _quant_ops as Q
 
-context.set_context(device_target='GPU', device_id=0)
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU', device_id=0)
 
 
 class Net(nn.Cell):
@@ -256,7 +256,7 @@ def test_fake_quant_grad10():
     x = np.array([-0.1, 0.0, 63.75, 63.8, -0.1, 0.0,
                   63.75, 63.8, -0.1, 0.0, 63.75, 63.8,
                   -0.1, 0.0, 63.75, 63.8, -0.1, 0.0,
-                  63.75, 63.8, -0.1, 0.0, 63.75, 63.8]).reshape(4, 3, 2, 1).astype(np.float32)
+                  63.75, 63.8, -0.1, 0.0, 63.75, 63.8]).reshape((4, 3, 2, 1)).astype(np.float32)
     min_val = np.array([-0.1, -0.1, -0.1, -0.1]).astype(np.float32)
     max_val = np.array([63.65, 63.65, 63.65, 63.65]).astype(np.float32)
     dout = read_dout.flatten()
@@ -286,7 +286,7 @@ def test_fake_quant_grad11():
     # WithVarsPerChannelDim4GradientNudgedDown_NarrowRange
     read_dout = np.random.uniform(-1, 1, size=[4, 3, 2, 1]).astype('float32')
     x = np.array([-0.1, 0.0, 63.5, 63.6, -0.1, 0.0, 63.5, 63.6, -0.1, 0.0, 63.5, 63.6, -0.1, 0.0, 63.5,
-                  63.6, -0.1, 0.0, 63.5, 63.6, -0.1, 0.0, 63.5, 63.6]).reshape(4, 3, 2, 1).astype(np.float32)
+                  63.6, -0.1, 0.0, 63.5, 63.6, -0.1, 0.0, 63.5, 63.6]).reshape((4, 3, 2, 1)).astype(np.float32)
     min_val = np.array([-0.1, -0.1, -0.1, -0.1]).astype(np.float32)
     max_val = np.array([63.4, 63.4, 63.4, 63.4]).astype(np.float32)
     dout = read_dout.flatten()
@@ -318,7 +318,7 @@ def test_fake_quant_grad12():
     x = np.array([-0.3, -0.25, 63.5, 63.6, -0.3, -0.25,
                   63.5, 63.6, -0.3, -0.25, 63.5, 63.6,
                   -0.3, -0.25, 63.5, 63.6, -0.3, -0.25,
-                  63.5, 63.6, -0.3, -0.25, 63.5, 63.6]).reshape(4, 3, 2, 1).astype(np.float32)
+                  63.5, 63.6, -0.3, -0.25, 63.5, 63.6]).reshape((4, 3, 2, 1)).astype(np.float32)
     min_val = np.array([-0.125, -0.125, -0.125, -0.125]).astype(np.float32)
     max_val = np.array([63.625, 63.625, 63.625, 63.625]).astype(np.float32)
     dout = read_dout.flatten()
@@ -350,7 +350,7 @@ def test_fake_quant_grad13():
     x = np.array([-0.3, -0.25, 63.25, 63.3, -0.3, -0.25,
                   63.25, 63.3, -0.3, -0.25, 63.25, 63.3,
                   -0.3, -0.25, 63.25, 63.3, -0.3, -0.25,
-                  63.25, 63.3, -0.3, -0.25, 63.25, 63.3]).reshape(4, 3, 2, 1).astype(np.float32)
+                  63.25, 63.3, -0.3, -0.25, 63.25, 63.3]).reshape((4, 3, 2, 1)).astype(np.float32)
     min_val = np.array([-0.125, -0.125, -0.125, -0.125]).astype(np.float32)
     max_val = np.array([63.375, 63.375, 63.375, 63.375]).astype(np.float32)
     dout = read_dout.flatten()
diff --git a/tests/st/ops/gpu/test_fake_quant_perlayer.py b/tests/st/ops/gpu/test_fake_quant_perlayer.py
index 661cea09253..b8ec461b0a4 100644
--- a/tests/st/ops/gpu/test_fake_quant_perlayer.py
+++ b/tests/st/ops/gpu/test_fake_quant_perlayer.py
@@ -21,7 +21,7 @@ from mindspore.common.tensor import Tensor
 import mindspore.nn as nn
 from mindspore.ops.operations import _quant_ops as Q
 
-context.set_context(device_target='GPU', device_id=0)
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU', device_id=0)
 
 
 class Net(nn.Cell):
diff --git a/tests/st/ops/gpu/test_fake_quant_perlayer_grad.py b/tests/st/ops/gpu/test_fake_quant_perlayer_grad.py
index f8330eff9f1..9289d2f954e 100644
--- a/tests/st/ops/gpu/test_fake_quant_perlayer_grad.py
+++ b/tests/st/ops/gpu/test_fake_quant_perlayer_grad.py
@@ -20,7 +20,7 @@ import mindspore.nn as nn
 import mindspore.context as context
 from mindspore.ops.operations import _quant_ops as Q
 
-context.set_context(device_target='GPU', device_id=0)
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU', device_id=0)
 
 
 class Net(nn.Cell):
diff --git a/tests/st/ops/gpu/test_in_top_k.py b/tests/st/ops/gpu/test_in_top_k.py
index c3f763eada4..32f7b753637 100644
--- a/tests/st/ops/gpu/test_in_top_k.py
+++ b/tests/st/ops/gpu/test_in_top_k.py
@@ -103,6 +103,7 @@ def test_in_top_k_float32():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_in_top_k_invalid_input():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     # k must be > 0
     with pytest.raises(ValueError):
         in_top_k_net = InTopKNet(0)
diff --git a/tests/st/ops/gpu/test_index_add_op.py b/tests/st/ops/gpu/test_index_add_op.py
index 73d99b17ee3..c3145d0a977 100644
--- a/tests/st/ops/gpu/test_index_add_op.py
+++ b/tests/st/ops/gpu/test_index_add_op.py
@@ -224,6 +224,7 @@ def test_index_add_int16():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_index_add_invalid_inputs():
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
     x = np.arange(2 * 3 * 4).reshape(2, 3, 4).astype(np.uint8)
     y = np.ones((2, 2, 4), dtype=np.uint8)
     with pytest.raises(TypeError):
diff --git a/tests/st/ops/gpu/test_loss.py b/tests/st/ops/gpu/test_loss.py
index 6e21dfd5876..f85867aa4a0 100644
--- a/tests/st/ops/gpu/test_loss.py
+++ b/tests/st/ops/gpu/test_loss.py
@@ -82,6 +82,8 @@ class CustomLoss(Loss):
         return self.get_loss(x, weights=2.0)
 
 def custom_loss(nptype):
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+
     loss = L1Loss()
     input_data = Tensor(np.array([[1, 2, 3], [2, 3, 4]]).astype(nptype))
     target_data = Tensor(np.array([[0, 2, 5], [3, 1, 1]]).astype(nptype))
diff --git a/tests/st/ops/gpu/test_lstm_op.py b/tests/st/ops/gpu/test_lstm_op.py
index de1197eeb20..c5800db6224 100644
--- a/tests/st/ops/gpu/test_lstm_op.py
+++ b/tests/st/ops/gpu/test_lstm_op.py
@@ -25,7 +25,7 @@ from mindspore.common.tensor import Tensor
 from mindspore.ops import composite as C
 from mindspore.ops import operations as P
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class LstmNet(nn.Cell):
diff --git a/tests/st/ops/gpu/test_one_hot_op.py b/tests/st/ops/gpu/test_one_hot_op.py
index 86a19a07bea..49ccc7993ba 100644
--- a/tests/st/ops/gpu/test_one_hot_op.py
+++ b/tests/st/ops/gpu/test_one_hot_op.py
@@ -21,7 +21,7 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import ms_function
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class NetOneHot(nn.Cell):
diff --git a/tests/st/ops/gpu/test_range_op.py b/tests/st/ops/gpu/test_range_op.py
index 92c62a275d3..aa0846533ad 100644
--- a/tests/st/ops/gpu/test_range_op.py
+++ b/tests/st/ops/gpu/test_range_op.py
@@ -21,6 +21,9 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.ops import operations as P
 
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+
 class RangeNet(nn.Cell):
     def __init__(self, maxlen=50):
         super(RangeNet, self).__init__()
@@ -34,8 +37,6 @@ class RangeNet(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_range_precision_end_equals_last_element():
-    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-
     range_net = RangeNet(100)
     ms_out = range_net(Tensor(1000.04, mstype.float32),
                        Tensor(1001.04, mstype.float32),
@@ -68,8 +69,6 @@ def test_range_precision_end_equals_last_element():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_range_int():
-    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-
     range_net = RangeNet()
     ms_out = range_net(Tensor(2, mstype.int32), Tensor(5, mstype.int32), Tensor(1, mstype.int32)).asnumpy()
     np_expected = np.array([2, 3, 4])
@@ -94,8 +93,6 @@ def test_range_int():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_range_float():
-    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-
     range_net = RangeNet()
     ms_out = range_net(Tensor(2.3, mstype.float32), Tensor(5.5, mstype.float32), Tensor(1.2, mstype.float32)).asnumpy()
     np_expected = np.array([2.3, 3.5, 4.7])
diff --git a/tests/st/ops/gpu/test_reduce_all_op.py b/tests/st/ops/gpu/test_reduce_all_op.py
index 9daf7a04c50..ed7dec41d78 100644
--- a/tests/st/ops/gpu/test_reduce_all_op.py
+++ b/tests/st/ops/gpu/test_reduce_all_op.py
@@ -39,8 +39,6 @@ x3 = np.array([[True, True], [True, False], [False, False]])
 axis3 = 1
 keep_dims3 = False
 
-context.set_context(device_target='GPU')
-
 
 class ReduceAll(nn.Cell):
     def __init__(self):
@@ -75,6 +73,7 @@ class ReduceAll(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_ReduceAll():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     reduce_all = ReduceAll()
     output = reduce_all()
 
diff --git a/tests/st/ops/gpu/test_reduce_any_op.py b/tests/st/ops/gpu/test_reduce_any_op.py
index 7da47b09ead..49f121c08cc 100644
--- a/tests/st/ops/gpu/test_reduce_any_op.py
+++ b/tests/st/ops/gpu/test_reduce_any_op.py
@@ -39,8 +39,6 @@ x3 = np.array([[True, True], [True, False], [False, False]])
 axis3 = 1
 keep_dims3 = False
 
-context.set_context(device_target='GPU')
-
 
 class ReduceAny(nn.Cell):
     def __init__(self):
@@ -75,6 +73,7 @@ class ReduceAny(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_ReduceAny():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     reduce_any = ReduceAny()
     output = reduce_any()
 
diff --git a/tests/st/ops/gpu/test_reduce_max_op.py b/tests/st/ops/gpu/test_reduce_max_op.py
index cb7d301f597..bfdc240ff80 100644
--- a/tests/st/ops/gpu/test_reduce_max_op.py
+++ b/tests/st/ops/gpu/test_reduce_max_op.py
@@ -63,8 +63,6 @@ axis8 = ()
 np_axis8 = None
 keep_dims8 = True
 
-context.set_context(device_target='GPU')
-
 
 class ReduceMax(nn.Cell):
     def __init__(self):
@@ -123,6 +121,7 @@ class ReduceMax(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_ReduceMax():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     reduce_max = ReduceMax()
     output = reduce_max()
 
diff --git a/tests/st/ops/gpu/test_reduce_mean_op.py b/tests/st/ops/gpu/test_reduce_mean_op.py
index 8fb6abdc3c0..0008dcdc898 100644
--- a/tests/st/ops/gpu/test_reduce_mean_op.py
+++ b/tests/st/ops/gpu/test_reduce_mean_op.py
@@ -84,8 +84,6 @@ axis14 = ()
 np_axis14 = None
 keep_dims14 = True
 
-context.set_context(device_target='GPU')
-
 
 class ReduceMean(nn.Cell):
     def __init__(self):
@@ -174,6 +172,7 @@ class ReduceMean(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_ReduceMean():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     reduce_mean = ReduceMean()
     output = reduce_mean()
 
diff --git a/tests/st/ops/gpu/test_reduce_min_op.py b/tests/st/ops/gpu/test_reduce_min_op.py
index 9492f19962a..a9d9cd6318a 100644
--- a/tests/st/ops/gpu/test_reduce_min_op.py
+++ b/tests/st/ops/gpu/test_reduce_min_op.py
@@ -63,8 +63,6 @@ axis8 = ()
 np_axis8 = None
 keep_dims8 = True
 
-context.set_context(device_target='GPU')
-
 
 class ReduceMin(nn.Cell):
     def __init__(self):
@@ -123,6 +121,7 @@ class ReduceMin(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_ReduceMin():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     reduce_min = ReduceMin()
     output = reduce_min()
 
diff --git a/tests/st/ops/gpu/test_reduce_sum_op.py b/tests/st/ops/gpu/test_reduce_sum_op.py
index c341b9f568a..862d587f78f 100644
--- a/tests/st/ops/gpu/test_reduce_sum_op.py
+++ b/tests/st/ops/gpu/test_reduce_sum_op.py
@@ -86,8 +86,6 @@ axis14 = ()
 np_axis14 = None
 keep_dims14 = True
 
-context.set_context(device_target='GPU')
-
 
 class ReduceSum(nn.Cell):
     def __init__(self):
@@ -176,6 +174,7 @@ class ReduceSum(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_ReduceSum():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     reduce_sum = ReduceSum()
     output = reduce_sum()
 
diff --git a/tests/st/ops/gpu/test_reverse_v2_op.py b/tests/st/ops/gpu/test_reverse_v2_op.py
index a1d57abd240..3666c5d0aa7 100644
--- a/tests/st/ops/gpu/test_reverse_v2_op.py
+++ b/tests/st/ops/gpu/test_reverse_v2_op.py
@@ -101,6 +101,7 @@ def test_reverse_v2_int64():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_reverse_v2_invalid_axis():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     x = Tensor(np.arange(60).reshape(1, 2, 3, 2, 5).astype(np.int32))
 
     with pytest.raises(ValueError) as info:
diff --git a/tests/st/ops/gpu/test_sampled_softmax_loss_op.py b/tests/st/ops/gpu/test_sampled_softmax_loss_op.py
index a7a56eebfdd..fd2219b5289 100644
--- a/tests/st/ops/gpu/test_sampled_softmax_loss_op.py
+++ b/tests/st/ops/gpu/test_sampled_softmax_loss_op.py
@@ -135,6 +135,7 @@ def test_sampled_softmax_loss_none_sampler():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_sampledsoftmaxloss_reduction_invalid():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     # Check 'reduction'
     with pytest.raises(ValueError):
         nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7, reduction="")
diff --git a/tests/st/ops/gpu/test_slice.py b/tests/st/ops/gpu/test_slice.py
index aef14084b5f..508f16f11b8 100644
--- a/tests/st/ops/gpu/test_slice.py
+++ b/tests/st/ops/gpu/test_slice.py
@@ -62,6 +62,7 @@ def test_slice_4d():
     x_np = np.random.randn(32, 24, 224, 224).astype(np.float32)
     output_np = x_np[:, 11:18, :, :]
 
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     x_ms = Tensor(x_np)
     net = SliceNet()
     output_ms = net(x_ms)
diff --git a/tests/st/ops/gpu/test_slice_grad.py b/tests/st/ops/gpu/test_slice_grad.py
index 8b1cd982ef8..6207f263bea 100644
--- a/tests/st/ops/gpu/test_slice_grad.py
+++ b/tests/st/ops/gpu/test_slice_grad.py
@@ -22,7 +22,7 @@ from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore.ops.operations import _grad_ops as G
 
-context.set_context(device_target='GPU')
+context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
 
 
 class SliceGrad(nn.Cell):
diff --git a/tests/st/ops/gpu/test_transpose_op.py b/tests/st/ops/gpu/test_transpose_op.py
index fe61bd05fcf..5aa517fe904 100644
--- a/tests/st/ops/gpu/test_transpose_op.py
+++ b/tests/st/ops/gpu/test_transpose_op.py
@@ -85,6 +85,7 @@ class Transpose_dynamic2(nn.Cell):
         return (out_1, out_2)
 
 def transpose1(nptype):
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
     transpose = Transpose(nptype)
     output = transpose()
     expect0 = np.array([[[0, 6, 12, 18, 24],
diff --git a/tests/st/ops/gpu/test_unsorted_segment_sum.py b/tests/st/ops/gpu/test_unsorted_segment_sum.py
index deafe8b72f8..6a317b27998 100644
--- a/tests/st/ops/gpu/test_unsorted_segment_sum.py
+++ b/tests/st/ops/gpu/test_unsorted_segment_sum.py
@@ -23,7 +23,6 @@ from mindspore.common import dtype as mstype
 from mindspore.ops.operations import _inner_ops as inner
 from mindspore.ops import operations as P
 
-context.set_context(device_target='GPU')
 
 class UnsortedSegmentSumNet(nn.Cell):
     def __init__(self, num_segments):
@@ -39,6 +38,7 @@ class UnsortedSegmentSumNet(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_1D():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     input_x = Tensor([1, 2, 3, 4], mstype.float32)
     segment_ids = Tensor([0, 0, 1, 2], mstype.int32)
     num_segments = 4
@@ -53,6 +53,7 @@ def test_1D():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_2D():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     input_x = Tensor([[1, 2, 3, 4],
                       [5, 6, 7, 8],
                       [9, 10, 11, 12]], mstype.float32)
@@ -72,6 +73,7 @@ def test_2D():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_3D():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     input_x = Tensor(np.arange(4 * 5 * 3, dtype=np.float32).reshape(4, 5, 3))
     segment_ids = Tensor([2, 1, 1, -1], mstype.int32)
     num_segments = 5
diff --git a/tests/st/ops/gpu/test_zeroslike_op.py b/tests/st/ops/gpu/test_zeroslike_op.py
index 0af1d363f89..fecdd78a650 100644
--- a/tests/st/ops/gpu/test_zeroslike_op.py
+++ b/tests/st/ops/gpu/test_zeroslike_op.py
@@ -157,6 +157,7 @@ def test_zeros_like_dynamic_float64():
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_zeros_like_dynamic_multiple_inputs():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     net = ZerosLikeDynamicNet()
 
     x = Tensor(np.arange(4).reshape(4).astype(np.float32))
diff --git a/tests/st/pynative/test_pynative_layernorm_input_and_argmaxwithvalue.py b/tests/st/pynative/test_pynative_layernorm_input_and_argmaxwithvalue.py
index 1427a8e20b2..0c506d3b47b 100644
--- a/tests/st/pynative/test_pynative_layernorm_input_and_argmaxwithvalue.py
+++ b/tests/st/pynative/test_pynative_layernorm_input_and_argmaxwithvalue.py
@@ -191,6 +191,7 @@ class ArgMaxWithValueFactory(OpsFactory):
         return input_grad.asnumpy()
 
     def forward_cmp(self):
+        context.set_context(mode=context.PYNATIVE_MODE, device_target=context.get_context('device_target'))
         out_numpy = self.forward_numpy_impl()
         out_mindspore = self.forward_mindspore_impl()
         allclose_nparray(out_numpy[0], out_mindspore[0], self.loss, self.loss)
diff --git a/tests/ut/python/nn/test_container.py b/tests/ut/python/nn/test_container.py
index c19cf573e99..2a4a18615be 100644
--- a/tests/ut/python/nn/test_container.py
+++ b/tests/ut/python/nn/test_container.py
@@ -18,7 +18,10 @@ import numpy as np
 import pytest
 
 import mindspore.nn as nn
-from mindspore import Tensor
+from mindspore import context, Tensor
+
+context.set_context(mode=context.PYNATIVE_MODE)
+
 
 weight = Tensor(np.ones([2, 2]))
 conv2 = nn.Conv2d(3, 64, (3, 3), stride=2, padding=0)
diff --git a/tests/ut/python/ops/test_array_ops.py b/tests/ut/python/ops/test_array_ops.py
index 2f2de0e0830..9bc30589ead 100644
--- a/tests/ut/python/ops/test_array_ops.py
+++ b/tests/ut/python/ops/test_array_ops.py
@@ -34,6 +34,8 @@ from ....mindspore_test_framework.pipeline.forward.compile_forward \
 from ....mindspore_test_framework.pipeline.forward.verify_exception \
     import pipeline_for_verify_exception_for_case_by_case_config
 
+context.set_context(mode=context.PYNATIVE_MODE)
+
 
 def test_expand_dims():
     input_tensor = Tensor(np.array([[2, 2], [2, 2]]))
diff --git a/tests/ut/python/ops/test_array_ops_check.py b/tests/ut/python/ops/test_array_ops_check.py
index 299c57a0754..af2b4db35c6 100755
--- a/tests/ut/python/ops/test_array_ops_check.py
+++ b/tests/ut/python/ops/test_array_ops_check.py
@@ -16,13 +16,15 @@
 import numpy as np
 
 import mindspore.nn as nn
-from mindspore import Tensor
+from mindspore import context, Tensor
 from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
 from ....mindspore_test_framework.mindspore_test import mindspore_test
 from ....mindspore_test_framework.pipeline.forward.compile_forward \
     import pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception
 
+context.set_context(mode=context.PYNATIVE_MODE)
+
 
 class ExpandDimsNet(nn.Cell):
     def __init__(self, axis):
diff --git a/tests/ut/python/ops/test_bprop_disorder.py b/tests/ut/python/ops/test_bprop_disorder.py
index b0392e5cd4d..f4f0c8cc373 100644
--- a/tests/ut/python/ops/test_bprop_disorder.py
+++ b/tests/ut/python/ops/test_bprop_disorder.py
@@ -27,7 +27,7 @@ from ....mindspore_test_framework.mindspore_test import mindspore_test
 from ....mindspore_test_framework.pipeline.forward.compile_forward \
     import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
 
-
+context.set_context(mode=context.PYNATIVE_MODE)
 grad_by_list_with_sens = C.GradOperation(get_by_list=True, sens_param=True)
 
 
diff --git a/tests/ut/python/pynative_mode/nn/test_container.py b/tests/ut/python/pynative_mode/nn/test_container.py
index 8b2e537b5c7..285b2eb788d 100644
--- a/tests/ut/python/pynative_mode/nn/test_container.py
+++ b/tests/ut/python/pynative_mode/nn/test_container.py
@@ -18,7 +18,10 @@ import numpy as np
 import pytest
 
 import mindspore.nn as nn
-from mindspore import Tensor
+from mindspore import context, Tensor
+
+context.set_context(mode=context.PYNATIVE_MODE)
+
 
 weight = Tensor(np.ones([2, 2]))
 conv2 = nn.Conv2d(3, 64, (3, 3), stride=2, padding=0)