!23365 add print op security

Merge pull request !23365 from fangzehua/print_sec

mindspore/_extends/parse/resources.py

@@ -21,6 +21,7 @@ import math
 from mindspore import RowTensor, SparseTensor
 from mindspore.ops import functional as F, composite as C
 from mindspore.ops.composite import multitype_ops
+from mindspore._c_expression import security
 from . import standard_method as M
 from . import trope as T
 from .namespace import CellNamespace
@@ -112,7 +113,6 @@ convert_object_map = {
     T.map:          C.Map(),
     T.partial:      F.partial,
     T.zip:          C.zip_operation,
-    T.print:        F.print_,
     T.enumerate:    M.enumerate_,
     T.isinstance:   M.isinstance_,
 
@@ -140,3 +140,6 @@ convert_object_map = {
     RowTensor:      F.make_row_tensor,
     SparseTensor:   F.make_sparse_tensor,
 }
+
+if not security.enable_security():
+    convert_object_map[T.print] = F.print_

mindspore/ops/functional.py

@@ -23,6 +23,7 @@ from .primitive import Primitive
 from . import operations as P
 from .operations import _grad_ops
 from .composite import GradOperation
+from .._c_expression import security
 
 typeof = Primitive('typeof')
 hastype = Primitive('hastype')
@@ -125,7 +126,8 @@ scalar_to_array = P.ScalarToArray()
 scalar_to_tensor = P.ScalarToTensor()
 tuple_to_array = P.TupleToArray()
 scalar_cast = P.ScalarCast()
-print_ = P.Print()
+if not security.enable_security():
+    print_ = P.Print()
 expand_dims = P.ExpandDims()
 transpose = P.Transpose()
 squeeze = P.Squeeze()

mindspore/ops/operations/debug_ops.py

@@ -17,12 +17,12 @@
 from types import FunctionType, MethodType
 
 from mindspore import context
+from mindspore._c_expression import security
 from ..._checkparam import Validator as validator
 from ..._checkparam import Rel
 from ...common import dtype as mstype
 from ..primitive import prim_attr_register, Primitive, PrimitiveWithInfer
 
-
 def _check_mode(class_name):
     """Check for PyNative mode."""
     mode = context.get_context('mode')
@@ -407,6 +407,9 @@ class Print(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self):
         """Initialize Print."""
+        if security.enable_security():
+            raise ValueError(
+                'The Print is not supported, please without `-s on` and recompile source.')
         self.add_prim_attr("side_effect_io", True)
 
     def __call__(self, *args):

model_zoo/official/cv/centerface/src/centerface.py

@@ -178,7 +178,6 @@ class CenterFaceLoss(nn.Cell):
         self.cls_loss = FocalLoss()
         self.reg_loss = SmoothL1LossNew()
         self.reg_loss_cmask = SmoothL1LossNewCMask()
-        self.print = P.Print()
 
     def construct(self, output_hm, output_wh, output_off, output_kps, hm, reg_mask, ind, wh, wight_mask, hm_offset,
                   hps_mask, landmarks):
@@ -211,7 +210,6 @@ class CenterFaceWithLossCell(nn.Cell):
         self.loss = CenterFaceLoss(self.config.wh_weight, self.config.reg_offset, self.config.off_weight,
                                    self.config.hm_weight, self.config.lm_weight)
         self.reduce_sum = P.ReduceSum()
-        self.print = P.Print()
 
     def construct(self, x, hm, reg_mask, ind, wh, wight_mask, hm_offset, hps_mask, landmarks):
         output_hm, output_wh, output_off, output_kps = self.centerface_network(x)
@@ -326,7 +324,6 @@ class CenterFaceWithNms(nn.Cell):
         self.maxpool2d = P.MaxPoolWithArgmax(kernel_size=3, strides=1, pad_mode='same')
         self.topk = P.TopK(sorted=True)
         self.reshape = P.Reshape()
-        self.print = P.Print()
         self.test_batch = self.config.test_batch_size
         self.k = self.config.K
 

model_zoo/official/cv/centerface/src/losses.py

@@ -26,7 +26,6 @@ class FocalLoss(nn.Cell):
         self.log = P.Log()
         self.pow = P.Pow()
         self.sum = P.ReduceSum()
-        self.print = P.Print()
 
     def construct(self, pred, gt):
         """Construct method"""

model_zoo/official/cv/centerface/src/mobile_v2.py

@@ -59,7 +59,6 @@ class ConvBNReLU(nn.Cell):
         layers = [conv, nn.BatchNorm2d(out_planes).add_flags_recursive(fp32=True), nn.ReLU6()]  #, momentum=0.9
         self.features = nn.SequentialCell(layers)
         self.in_planes = in_planes
-        self.print = P.Print()
 
     def construct(self, x):
         x = self.features(x)

model_zoo/official/cv/ctpn/src/CTPN/bbox_assign_sample.py

@@ -96,7 +96,6 @@ class BboxAssignSample(nn.Cell):
         self.range_pos_size = Tensor(np.arange(self.num_expected_pos).astype(np.float16))
         self.check_gt_one = Tensor(np.array(-1 * np.ones((self.num_gts, 4)), dtype=np.float16))
         self.check_anchor_two = Tensor(np.array(-2 * np.ones((self.num_bboxes, 4)), dtype=np.float16))
-        self.print = P.Print()
 
 
     def construct(self, gt_bboxes_i, gt_labels_i, valid_mask, bboxes, gt_valids):

model_zoo/official/cv/ctpn/src/CTPN/proposal_generator.py

@@ -124,7 +124,6 @@ class Proposal(nn.Cell):
         self.min_float_num = -65536.0
         self.topK_mask = Tensor(self.min_float_num * np.ones(total_max_topk_input, np.float16))
         self.shape = P.Shape()
-        self.print = P.Print()
 
     def construct(self, rpn_cls_score_total, rpn_bbox_pred_total, anchor_list):
         proposals_tuple = ()

model_zoo/official/cv/ctpn/src/CTPN/rpn.py

@@ -52,7 +52,6 @@ class RpnRegClsBlock(nn.Cell):
         self.shape1 = (-1, config.num_step, config.rnn_batch_size)
         self.shape2 = (config.batch_size, -1, config.rnn_batch_size, config.num_step)
         self.transpose = P.Transpose()
-        self.print = P.Print()
 
     def construct(self, x):
         x = self.reshape(x, self.shape)
@@ -143,7 +142,6 @@ class RPN(nn.Cell):
         self.loss = Tensor(np.zeros((1,)).astype(np.float16))
         self.clsloss = Tensor(np.zeros((1,)).astype(np.float16))
         self.regloss = Tensor(np.zeros((1,)).astype(np.float16))
-        self.print = P.Print()
 
     def _make_rpn_layer(self, num_layers, in_channels, feat_channels, num_anchors, cls_out_channels):
         """

model_zoo/official/cv/openpose/src/loss.py

@@ -120,7 +120,6 @@ class openpose_loss(MyLoss):
         self.square = P.Square()
         self.reduceMean = P.ReduceMean()
         self.reduceSum = P.ReduceSum()
-        self.print = P.Print()
         self.shape = P.Shape()
         self.maxoftensor = P.ArgMaxWithValue(-1)
 

model_zoo/official/cv/openpose/src/openposenet.py

@@ -37,7 +37,6 @@ class OpenPoseNet(nn.Cell):
         self.stage_6 = Stage_x()
         self.shape = P.Shape()
         self.cat = P.Concat(axis=1)
-        self.print = P.Print()
         if loadvgg and vggpath:
             param_dict = load_checkpoint(vggpath)
             param_dict_new = {}

model_zoo/official/cv/posenet/src/posenet.py

@@ -120,7 +120,6 @@ class PoseNet(nn.Cell):
         self.dropout5 = nn.Dropout(0.5)
         self.cls_fc_pose_xyz = nn.Dense(2048, 3)
         self.cls_fc_pose_wpqr = nn.Dense(2048, 4)
-        self.print = P.Print()
 
     def construct(self, x):
         """construct"""

model_zoo/official/cv/vit/src/vit.py

@@ -82,7 +82,6 @@ class DropPath(Cell):
         self.rand = P.UniformReal(seed=seed) # seed must be 0, if set to other value, it's not rand for multiple call
         self.shape = P.Shape()
         self.floor = P.Floor()
-        self.print = P.Print()
 
     def construct(self, x):
         if self.training:

model_zoo/official/nlp/fasttext/src/fasttext_train.py

@@ -69,7 +69,6 @@ class FastTextNetWithLoss(nn.Cell):
         self.fasttext = FastText(vocab_size, embedding_dims, num_class)
         self.loss_func = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
         self.squeeze = P.Squeeze(axis=1)
-        self.print = P.Print()
 
     def construct(self, src_tokens, src_tokens_lengths, label_idx):
         """

model_zoo/official/nlp/gru/src/seq2seq.py

@@ -84,7 +84,6 @@ class Encoder(nn.Cell):
         self.fc = nn.Dense(2*self.hidden_size, self.hidden_size).to_float(config.compute_type)
         self.shape = P.Shape()
         self.transpose = P.Transpose()
-        self.p = P.Print()
         self.cast = P.Cast()
         self.text_len = config.max_length
         self.squeeze = P.Squeeze(axis=0)
@@ -131,7 +130,6 @@ class Decoder(nn.Cell):
         self.text_len = config.max_length
         self.shape = P.Shape()
         self.transpose = P.Transpose()
-        self.p = P.Print()
         self.cast = P.Cast()
         self.concat = P.Concat(axis=2)
         self.squeeze = P.Squeeze(axis=0)

model_zoo/official/nlp/textcnn/src/textcnn.py

@@ -115,7 +115,6 @@ class TextCNN(nn.Cell):
 
         self.fc = nn.Dense(96*3, self.num_classes)
         self.drop = nn.Dropout(keep_prob=0.5)
-        self.print = P.Print()
         self.reducemax = P.ReduceMax(keep_dims=False)
 
     def make_layer(self, kernel_height):

model_zoo/research/cv/DDM/net/deeplabv2_mindspore.py

@@ -124,7 +124,6 @@ class ResNetMulti(nn.Cell):
             self.layer5 = ClassifierModule(1024, [6, 12, 18, 24], [6, 12, 18, 24], num_classes)
 
         self.layer6 = ClassifierModule(2048, [6, 12, 18, 24], [6, 12, 18, 24], num_classes)
-        self.p = P.Print()
         self.shape = Shape()
         self.pad = nn.Pad(((0, 0), (0, 0), (1, 1), (1, 1)), "CONSTANT")
 

model_zoo/research/cv/FaceAttribute/src/FaceAttribute/loss_factory.py

@@ -42,7 +42,6 @@ class CriterionsFaceAttri(nn.Cell):
 
         # loss
         self.ce_ignore_loss = CrossEntropyWithIgnoreIndex()
-        self.printn = P.Print()
 
     def construct(self, x0, x1, x2, label):
         '''Construct function.'''

model_zoo/research/cv/FaceAttribute/train.py

@@ -26,7 +26,6 @@ from mindspore.nn import TrainOneStepCell
 from mindspore.context import ParallelMode
 from mindspore.train.callback import ModelCheckpoint, RunContext, CheckpointConfig
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.ops import operations as P
 from mindspore.common import dtype as mstype
 from src.FaceAttribute.resnet18 import get_resnet18
 from src.FaceAttribute.loss_factory import get_loss
@@ -55,7 +54,6 @@ class BuildTrainNetwork(nn.Cell):
         super(BuildTrainNetwork, self).__init__()
         self.network = my_network
         self.criterion = my_criterion
-        self.print = P.Print()
 
     def construct(self, input_data, label):
         logit0, logit1, logit2 = self.network(input_data)

model_zoo/research/cv/FaceDetection/src/FaceDetection/yolo_postprocess.py

@@ -42,7 +42,6 @@ class YoloPostProcess(Cell):
     """
     def __init__(self, num_classes, cur_anchors, conf_thresh, network_size, reduction, anchors_mask):
         super(YoloPostProcess, self).__init__()
-        self.print = P.Print()
         self.num_classes = num_classes
         self.anchors = cur_anchors
         self.conf_thresh = conf_thresh

model_zoo/research/cv/FaceDetection/src/network_define.py

@@ -174,7 +174,6 @@ class BuildTestNetwork(nn.Cell):
     '''BuildTestNetwork'''
     def __init__(self, network, reduction_0, reduction_1, reduction_2, anchors, anchors_mask, num_classes, args):
         super(BuildTestNetwork, self).__init__()
-        self.print = P.Print()
         self.network = network
         self.reduction_0 = reduction_0
         self.reduction_1 = reduction_1

model_zoo/research/cv/IPT/src/utils.py

@@ -37,7 +37,6 @@ class MyTrain(nn.Cell):
         self.model = model
         self.con_loss = con_loss
         self.criterion = criterion
-        self.p = P.Print()
         self.cast = P.Cast()
 
     def construct(self, lr, hr, idx):

model_zoo/research/cv/ManiDP/src/resnet.py

@@ -91,7 +91,6 @@ class MaskBlock(nn.Cell):
         self.relu = P.ReLU()
 
         self.thre = thres[num]
-        self.print = P.Print()
 
     def construct(self, x):
         """construct"""

model_zoo/research/cv/inception_resnet_v2/src/inception_resnet_v2.py

@@ -245,7 +245,6 @@ class InceptionC(nn.Cell):
         self.concat = P.Concat(1)
         if not self.noReLU:
             self.relu = nn.ReLU()
-        self.print = P.Print()
 
     def construct(self, x):
         x0 = self.branch0(x)

tests/st/auto_monad/test_auto_monad.py

@@ -28,6 +28,7 @@ from mindspore.common.parameter import Parameter
 from mindspore.common.initializer import initializer
 from mindspore.ops.primitive import constexpr
 from capture import Capture, capture, check_output
+from tests.security_utils import security_off_wrap
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
@@ -48,6 +49,7 @@ def _with_save_graphs():
     clean_all_ir_files('./')
 
 
+@security_off_wrap
 def test_print():
     class Print(Cell):
         def __init__(self):
@@ -71,6 +73,7 @@ def test_print():
     check_output(cap.output, patterns)
 
 
+@security_off_wrap
 def test_print_add():
     class Print_Add(Cell):
         def __init__(self):
@@ -98,6 +101,7 @@ def test_print_add():
     check_output(cap.output, patterns)
 
 
+@security_off_wrap
 def test_print_assign():
     class Print_Assign(Cell):
         def __init__(self):
@@ -125,6 +129,7 @@ def test_print_assign():
     check_output(cap.output, patterns)
 
 
+@security_off_wrap
 def test_print_assign_add():
     class Print_Assign_Add(Cell):
         def __init__(self):
@@ -155,6 +160,7 @@ def test_print_assign_add():
     check_output(cap.output, patterns)
 
 
+@security_off_wrap
 def test_print_while():
     class Print_While(Cell):
         def __init__(self):
@@ -189,6 +195,7 @@ def test_print_while():
     check_output(cap.output, patterns)
 
 
+@security_off_wrap
 def test_print_if():
     class Print_If(Cell):
         def __init__(self):
@@ -219,6 +226,7 @@ def test_print_if():
     check_output(cap.output, patterns)
 
 
+@security_off_wrap
 def test_print_assign_while():
     class Print_Assign_While(Cell):
         def __init__(self):
@@ -262,6 +270,7 @@ def test_print_assign_while():
     check_output(cap.output, patterns)
 
 
+@security_off_wrap
 def test_print_assign_if():
     class Print_Assign_If(Cell):
         def __init__(self):
@@ -517,6 +526,7 @@ def test_for():
     np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
 
 
+@security_off_wrap
 def test_print_for():
     class Print_For(Cell):
         def __init__(self):
@@ -553,6 +563,7 @@ def test_print_for():
     check_output(cap.output, patterns)
 
 
+@security_off_wrap
 def test_print_assign_for():
     class Print_Assign_For(Cell):
         def __init__(self):
@@ -739,6 +750,7 @@ def test_multi_assign_addn():
     np.testing.assert_almost_equal(out.asnumpy(), expect.asnumpy())
 
 
+@security_off_wrap
 def test_multi_assign_print():
     class Multi_Assign_Print(Cell):
         def __init__(self):

tests/st/auto_monad/test_auto_monad_mindtester.py

@@ -24,6 +24,7 @@ from mindspore.common.initializer import initializer
 from mindspore.train.model import Model
 from mindspore.ops.composite import GradOperation
 from mindspore.common import ParameterTuple
+from tests.security_utils import security_off_wrap
 
 context.set_context(mode=context.GRAPH_MODE)
 
@@ -346,6 +347,7 @@ class SideEffectIOCellAddnNet(Cell):
         return grad_out
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training
@@ -460,7 +462,7 @@ class SideEffectPrintInHighOrdeAddnNet(Cell):
         grad_out = grad_net(params, grad_ys)
         return grad_out
 
-
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_arm_ascend_training
 @pytest.mark.platform_x86_ascend_training

tests/st/ops/gpu/test_print_op.py

@@ -21,6 +21,7 @@ from mindspore import Tensor
 import mindspore.nn as nn
 from mindspore.ops import operations as P
 import mindspore.context as context
+from tests.security_utils import security_off_wrap
 
 
 class PrintNetOneInput(nn.Cell):
@@ -53,6 +54,8 @@ class PrintNetIndex(nn.Cell):
         return x
 
 
+
+@security_off_wrap
 def print_testcase(nptype):
     # large shape
     x = np.arange(20808).reshape(6, 3, 34, 34).astype(nptype)
@@ -85,6 +88,7 @@ class PrintNetString(nn.Cell):
         return x
 
 
+@security_off_wrap
 def print_testcase_string(nptype):
     x = np.ones(18).astype(nptype)
     y = np.arange(9).reshape(3, 3).astype(nptype)
@@ -106,6 +110,7 @@ class PrintTypes(nn.Cell):
         return x
 
 
+@security_off_wrap
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -118,6 +123,7 @@ def test_print_multiple_types():
     net(x, y, z)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -125,6 +131,7 @@ def test_print_bool():
     print_testcase(np.bool)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -132,6 +139,7 @@ def test_print_int8():
     print_testcase(np.int8)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -139,6 +147,7 @@ def test_print_int16():
     print_testcase(np.int16)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -146,6 +155,7 @@ def test_print_int32():
     print_testcase(np.int32)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -153,6 +163,7 @@ def test_print_int64():
     print_testcase(np.int64)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -160,6 +171,7 @@ def test_print_uint8():
     print_testcase(np.uint8)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -167,6 +179,7 @@ def test_print_uint16():
     print_testcase(np.uint16)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -174,6 +187,7 @@ def test_print_uint32():
     print_testcase(np.uint32)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -181,6 +195,7 @@ def test_print_uint64():
     print_testcase(np.uint64)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -188,6 +203,7 @@ def test_print_float16():
     print_testcase(np.float16)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -195,6 +211,7 @@ def test_print_float32():
     print_testcase(np.float32)
 
 
+@security_off_wrap
 @pytest.mark.level1
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard

tests/ut/python/parallel/test_print.py

@@ -19,7 +19,7 @@ from mindspore import context, Tensor, Parameter
 from mindspore.common.api import _cell_graph_executor
 from mindspore.nn import Cell, TrainOneStepCell, Momentum, BatchNorm2d, BatchNorm1d
 from mindspore.ops import operations as P
-
+from tests.security_utils import security_off_wrap
 
 class Net(Cell):
     def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride,
@@ -53,6 +53,7 @@ def compile_net(net):
     context.reset_auto_parallel_context()
 
 
+@security_off_wrap
 def test_batchnorm_data_parallel():
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
@@ -61,6 +62,7 @@ def test_batchnorm_data_parallel():
     compile_net(net)
 
 
+@security_off_wrap
 def test_batchnorm_model_parallel1():
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
@@ -69,6 +71,7 @@ def test_batchnorm_model_parallel1():
     compile_net(net)
 
 
+@security_off_wrap
 def test_batchnorm_model_parallel2():
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
     strategy1 = ((2, 2, 2, 2), (2, 2, 1, 1))

tests/ut/python/pipeline/infer/test_auto_monad.py

@@ -9,6 +9,7 @@ from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
 from mindspore.common.parameter import Parameter, ParameterTuple
+from tests.security_utils import security_off_wrap
 
 grad_all_list = C.GradOperation(get_all=True, get_by_list=True)
 grad_by_list = C.GradOperation(get_by_list=True)
@@ -117,6 +118,7 @@ def test_insert_gradient_of():
     print(grad_net(Tensor(input_data)))
 
 
+@security_off_wrap
 def test_user_defined_bprop():
     class UserDefinedNet(nn.Cell):
         def __init__(self):
@@ -151,6 +153,7 @@ def test_user_defined_bprop():
 
 
 # user defined bprop don't have the same size of parameters with primal's
+@security_off_wrap
 def test_user_defined_bad_bprop():
     class UserDefinedNet(nn.Cell):
         def __init__(self):
@@ -186,6 +189,7 @@ def test_user_defined_bad_bprop():
 
 
 # shoul compile success and Print in presented in the final function graph.
+@security_off_wrap
 @pytest.mark.skip(reason="isolated nodes exception")
 def test_unused_var():
     class UnusedVar(nn.Cell):
@@ -212,6 +216,7 @@ def test_unused_var():
 
 
 # shoul compile success and Print in presented in the final function graph.
+@security_off_wrap
 @pytest.mark.skip(reason="isolated nodes exception")
 def test_hof_unused_var():
     class UnusedVar(nn.Cell):
@@ -241,6 +246,7 @@ def test_hof_unused_var():
 
 
 # shoul compile success and Print in presented in the final function graph.
+@security_off_wrap
 @pytest.mark.skip(reason="isolated nodes exception")
 def test_partial_hof_unused_var():
     class UnusedVar(nn.Cell):

tests/ut/python/pynative_mode/test_stop_gradient.py

@@ -27,6 +27,7 @@ from mindspore.ops import composite as C
 from mindspore.ops import operations as P
 from mindspore.ops.functional import stop_gradient
 from mindspore.ops.primitive import prim_attr_register, PrimitiveWithInfer
+from tests.security_utils import security_off_wrap
 from ..ut_filter import non_graph_engine
 from ....mindspore_test_framework.utils.bprop_util import bprop
 
@@ -429,6 +430,7 @@ def test_stop_gradient_11():
               Tensor(np.ones([2]).astype(np.float32)))
 
 
+@security_off_wrap
 def test_stop_print():
     class StopPrint(nn.Cell):
         def __init__(self):

tests/ut/python/utils/test_serialize.py

@@ -34,6 +34,7 @@ from mindspore.ops import operations as P
 from mindspore.train.callback import _CheckpointManager
 from mindspore.train.serialization import save_checkpoint, load_checkpoint, load_param_into_net, \
      export, _save_graph, load
+from tests.security_utils import security_off_wrap
 from ..ut_filter import non_graph_engine
 
 context.set_context(mode=context.GRAPH_MODE, print_file_path="print/print.pb")
@@ -419,6 +420,7 @@ class PrintNet(nn.Cell):
         return int8, uint8, int16, uint16, int32, uint32, int64, uint64, flt16, flt32, flt64, bool_, scale1, scale2
 
 
+@security_off_wrap
 def test_print():
     print_net = PrintNet()
     int8 = Tensor(np.random.randint(100, size=(10, 10), dtype="int8"))