Add testcase for PyNative tasksink

tests/st/pynative/test_pynative_resnet50_ascend.py

@@ -20,6 +20,7 @@ from multiprocessing import Process, Queue
 import numpy as np
 import pytest
 
+from mindspore.common import JitConfig
 import mindspore.common.dtype as mstype
 import mindspore.dataset as ds
 import mindspore.dataset.transforms as C
@@ -415,7 +416,12 @@ class GradWrap(Cell):
         return grad_by_list(self.network, weights)(x, label)
 
 
-def test_pynative_resnet50():
+def test_pynative_resnet50(task_sink=False):
+    """
+    Feature: PyNative ResNet50
+    Description: test PyNative ResNet50
+    Expectation: success
+    """
     batch_size = 32
     num_classes = 10
     loss_scale = 128
@@ -431,6 +437,8 @@ def test_pynative_resnet50():
 
     loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
     loss_scale = FixedLossScaleManager(loss_scale=loss_scale, drop_overflow_update=False)
+    if task_sink:
+        net.set_jit_config(JitConfig(jit_level="O2"))
     model = Model(net, loss_fn=loss, optimizer=optimizer, loss_scale_manager=loss_scale, metrics={'acc'},
                   amp_level="O2", keep_batchnorm_fp32=False)
 
@@ -453,7 +461,7 @@ def test_pynative_resnet50_with_mpi():
     context.reset_auto_parallel_context()
     context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=False, device_num=8)
 
-    good_steps = test_pynative_resnet50()
+    good_steps = test_pynative_resnet50(True)
     assert good_steps > 10
 
 
@@ -469,7 +477,7 @@ def test_pynative_resnet50_with_env(queue, device_id, device_num):
     context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=False,
                                       device_num=device_num)
 
-    good_steps = test_pynative_resnet50()
+    good_steps = test_pynative_resnet50(False)
     queue.put(good_steps)