[test] Fix GRAPH_MODE not restored after PYNATIVE_MODE is set

tests/st/auto_monad/test_auto_monad_mindtester.py

@@ -550,9 +550,12 @@ def test_side_effect_grad_two_addn_switch():
     inputs = Tensor([9.0], ms.float32)
     out1 = net.grad_mindspore_impl(inputs, grad_ys)
     net = SideEffectTwoAddnSwitchNet()
-    context.set_context(mode=context.PYNATIVE_MODE)
-    out2 = net.grad_mindspore_impl(inputs, grad_ys)
-    allclose_nparray(out1[0][0].asnumpy(), out2[0][0].asnumpy(), 0.001, 0.001)
+    try:
+        context.set_context(mode=context.PYNATIVE_MODE)
+        out2 = net.grad_mindspore_impl(inputs, grad_ys)
+        allclose_nparray(out1[0][0].asnumpy(), out2[0][0].asnumpy(), 0.001, 0.001)
+    finally:
+        context.set_context(mode=context.GRAPH_MODE)
 
 
 class SideEffectGradIfNet(Cell):
@@ -590,9 +593,12 @@ def test_side_effect_grad_if():
     inputs = Tensor([9.0], ms.float32)
     out1 = net.grad_mindspore_impl(inputs, grad_ys)
     net = SideEffectGradIfNet()
-    context.set_context(mode=context.PYNATIVE_MODE)
-    out2 = net.grad_mindspore_impl(inputs, grad_ys)
-    allclose_nparray(out1.asnumpy(), out2.asnumpy(), 0.001, 0.001)
+    try:
+        context.set_context(mode=context.PYNATIVE_MODE)
+        out2 = net.grad_mindspore_impl(inputs, grad_ys)
+        allclose_nparray(out1.asnumpy(), out2.asnumpy(), 0.001, 0.001)
+    finally:
+        context.set_context(mode=context.GRAPH_MODE)
 
 
 class OneInputBprop(Cell):
@@ -683,8 +689,11 @@ def test_side_effect_grad_control_flow_assign_depend_while_net():
     inputs2 = Tensor([6.0], ms.float32)
     inputs3 = Tensor([3.0], ms.float32)
     out1 = net.grad_mindspore_impl(inputs1, inputs2, inputs3, grad_ys)
-    context.set_context(mode=context.PYNATIVE_MODE)
-    net = SideEffectControlFlowAssignDependWhileNet()
-    out2 = net.grad_mindspore_impl(inputs1, inputs2, inputs3, grad_ys)
-    allclose_nparray(out1[0][0].asnumpy(), out2[0][0].asnumpy(), 0.001, 0.001)
-    allclose_nparray(out1[1][0].asnumpy(), out2[1][0].asnumpy(), 0.001, 0.001)
+    try:
+        context.set_context(mode=context.PYNATIVE_MODE)
+        net = SideEffectControlFlowAssignDependWhileNet()
+        out2 = net.grad_mindspore_impl(inputs1, inputs2, inputs3, grad_ys)
+        allclose_nparray(out1[0][0].asnumpy(), out2[0][0].asnumpy(), 0.001, 0.001)
+        allclose_nparray(out1[1][0].asnumpy(), out2[1][0].asnumpy(), 0.001, 0.001)
+    finally:
+        context.set_context(mode=context.GRAPH_MODE)

tests/st/auto_monad/test_effect_random.py

@@ -29,6 +29,7 @@ class Sampling(nn.Cell):
     """
     Test class: sample of Normal distribution.
     """
+
     def __init__(self, shape, seed=0):
         super(Sampling, self).__init__()
         self.n1 = msd.Normal(0, 1, seed=seed, dtype=dtype.float32)
@@ -400,6 +401,8 @@ class RandomChoiceWithMaskNet(nn.Cell):
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
 def test_random_choice_with_mask():
+    mode = context.get_context('mode')
+    assert (mode == context.GRAPH_MODE), 'GRAPH_MODE required but got ' + str(mode)
     net = RandomChoiceWithMaskNet()
     x = Tensor(np.array([[1, 0, 1, 0], [0, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 1]]).astype(np.bool))
     index1, index2, index3 = net(x)

tests/st/networks/models/resnet50/test_resnet50_imagenet.py

@@ -312,6 +312,11 @@ def train_process_thor(q, device_id, epoch_size, device_num, enable_hccl):
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_single
 def test_resnet_and_resnet_thor_imagenet_4p():
+    # reset context
+    context.set_context(save_graphs=False, enable_graph_kernel=False, enable_sparse=False)
+    context.reset_auto_parallel_context()
+    context.reset_ps_context()
+
     q = Queue()
     q2 = Queue()