Upgrade ps ci cases

tests/st/ps/cmp_sparse_embedding/shell_run_test.sh

@@ -22,7 +22,6 @@ export MS_WORKER_NUM=$2
 export MS_SERVER_NUM=$3
 export MS_SCHED_HOST=$4
 export MS_SCHED_PORT=$5
-
 export MS_ROLE=MS_SCHED
 for((i=0;i<1;i++));
 do

tests/st/ps/cmp_sparse_embedding/test_cmp_sparse_embedding.py

@@ -14,7 +14,6 @@
 # ============================================================================
 
 import os
-import sys
 import argparse
 import numpy as np
 
@@ -26,10 +25,11 @@ from mindspore.nn import TrainOneStepCell, WithLossCell
 from mindspore.nn.optim import Adam
 from mindspore.common import set_seed
 from mindspore.ops import operations as P
-from mindspore.parallel._ps_context import _is_role_pserver, _is_role_worker
+from mindspore.parallel._ps_context import _is_role_worker
+from mindspore.communication.management import init
 
 parser = argparse.ArgumentParser(description="test_sparse_embedding")
-parser.add_argument("--device_target", type=str, default="Ascend")
+parser.add_argument("--device_target", type=str, default="GPU")
 args, _ = parser.parse_known_args()
 device_target = args.device_target
 context.set_context(
@@ -71,12 +71,8 @@ def do_sparse_embedding(ps=False):
     for _ in range(epoch):
         data = Tensor(np.random.randint(-5, 15, (32, 3), np.int32))
         label = Tensor(np.random.randint(0, 9, (32), np.int32))
-        if _is_role_pserver():
-            train_network(data, label)
-            sys.exit()
-        else:
-            loss = train_network(data, label).asnumpy()
-            losses.append(loss)
+        loss = train_network(data, label).asnumpy()
+        losses.append(loss)
     print(losses)
     return losses
 
@@ -84,6 +80,7 @@ def do_sparse_embedding(ps=False):
 envs = os.environ
 if __name__ == "__main__":
     set_seed(0)
+    init()
     ps_loss = do_sparse_embedding(True)
 
     if _is_role_worker():
@@ -92,4 +89,4 @@ if __name__ == "__main__":
         no_ps_loss = do_sparse_embedding()
         context.set_ps_context(enable_ps=True)
 
-    assert np.allclose(ps_loss, no_ps_loss, rtol=1.0e-6, atol=1.0e-6)
+        assert np.allclose(ps_loss, no_ps_loss, rtol=1.0e-6, atol=1.0e-6)

tests/st/ps/cmp_sparse_embedding/test_entry_cmp_sparse_embedding.py

@@ -13,13 +13,8 @@
 # limitations under the License.
 # ============================================================================
 import os
-import pytest
 
 
-@pytest.mark.level1
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
 def test_cmp_sparse_embedding():
-    return_code = os.system("bash shell_run_test.sh Ascend 1 1 127.0.0.1 8081")
+    return_code = os.system("bash shell_run_test.sh GPU 1 1 127.0.0.1 8081")
     assert return_code == 0

tests/st/ps/full_ps/shell_run_test.sh

@@ -23,8 +23,8 @@ export MS_WORKER_NUM=$3
 export MS_SERVER_NUM=$4
 export MS_SCHED_HOST=$5
 export MS_SCHED_PORT=$6
-
 export MS_ROLE=MS_SCHED
+export fusion=True
 for((i=0;i<1;i++));
 do
   rm -rf ${execute_path}/sched_$i/

tests/st/ps/full_ps/test_entry_full_ps_lenet.py

@@ -13,15 +13,10 @@
 # limitations under the License.
 # ============================================================================
 import os
-import pytest
 
 
-@pytest.mark.level0
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
 def test_full_ps_ascend_lenet():
     return_code = os.system(
-        "bash shell_run_test.sh Ascend /home/workspace/mindspore_dataset/mnist 1 1 127.0.0.1 8082"
+        "bash shell_run_test.sh GPU /home/workspace/mindspore_dataset/mnist 1 1 127.0.0.1 8082"
     )
     assert return_code == 0

tests/st/ps/full_ps/test_full_ps_lenet.py

@@ -27,9 +27,10 @@ from mindspore.nn.metrics import Accuracy
 from mindspore.train import Model
 from mindspore.train.callback import LossMonitor
 from mindspore.common.initializer import TruncatedNormal
+from mindspore.communication.management import init
 
 parser = argparse.ArgumentParser(description='test_ps_lenet')
-parser.add_argument("--device_target", type=str, default="Ascend")
+parser.add_argument("--device_target", type=str, default="GPU")
 parser.add_argument("--dataset_path", type=str, default="/home/workspace/mindspore_dataset/mnist")
 args, _ = parser.parse_known_args()
 device_target = args.device_target
@@ -122,6 +123,7 @@ def create_dataset(data_path, batch_size=32, repeat_size=1,
     return mnist_ds
 
 if __name__ == "__main__":
+    init()
     network = LeNet5(10)
     network.set_param_ps()
     net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")

tests/st/ps/part_ps/shell_run_test.sh

@@ -23,8 +23,8 @@ export MS_WORKER_NUM=$3
 export MS_SERVER_NUM=$4
 export MS_SCHED_HOST=$5
 export MS_SCHED_PORT=$6
-
 export MS_ROLE=MS_SCHED
+export fusion=True
 for((i=0;i<1;i++));
 do
   rm -rf ${execute_path}/sched_$i/

tests/st/ps/part_ps/test_entry_ps_embedding_heterogeneous_conv2d_adam.py

@@ -13,11 +13,10 @@
 # limitations under the License.
 # ============================================================================
 import os
-import pytest
 
 
 def test_ps_embedding_heterogeneous_conv2d_adam():
     return_code = os.system(
-        "bash shell_run_test.sh Ascend /home/workspace/mindspore_dataset/mnist 1 1 127.0.0.1 8085"
+        "bash shell_run_test.sh GPU /home/workspace/mindspore_dataset/mnist 1 1 127.0.0.1 8085"
     )
     assert return_code == 0

tests/st/ps/part_ps/test_ps_embedding_heterogeneous_conv2d_adam.py

@@ -36,9 +36,10 @@ import mindspore.ops.operations as op
 from mindspore.common.parameter import Parameter
 from mindspore.train import Model
 from mindspore.common import set_seed
+from mindspore.communication.management import init
 
 parser = argparse.ArgumentParser(description='test_ps_lenet')
-parser.add_argument("--device_target", type=str, default="Ascend")
+parser.add_argument("--device_target", type=str, default="GPU")
 parser.add_argument("--dataset_path", type=str, default="/home/workspace/mindspore_dataset/mnist")
 args, _ = parser.parse_known_args()
 device_target = args.device_target
@@ -180,5 +181,6 @@ class NetFactory:
 
 
 if __name__ == "__main__":
+    init()
     fact = NetFactory()
     fact.part_cmp()