diff --git a/tests/st/auto_parallel/env.sh b/tests/st/auto_parallel/env.sh
index 681c466f43c..475c9f5ffbc 100644
--- a/tests/st/auto_parallel/env.sh
+++ b/tests/st/auto_parallel/env.sh
@@ -13,9 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-LOCAL_HIAI=/usr/local/HiAI
-export TBE_IMPL_PATH=${LOCAL_HIAI}/runtime/ops/op_impl/built-in/ai_core/tbe/impl/
-export LD_LIBRARY_PATH=${LOCAL_HIAI}/runtime/lib64/:${LD_LIBRARY_PATH}
+LOCAL_HIAI=/usr/local/Ascend
+export TBE_IMPL_PATH=${LOCAL_HIAI}/runtime/ops/op_impl/built-in/ai_core/tbe/impl/:${TBE_IMPL_PATH}
+export LD_LIBRARY_PATH=${LOCAL_HIAI}/runtime/lib64/:${LOCAL_HIAI}/add-ons/:${LD_LIBRARY_PATH}
 export PATH=${LOCAL_HIAI}/runtime/ccec_compiler/bin/:${PATH}
 export PYTHONPATH=${LOCAL_HIAI}/runtime/ops/op_impl/built-in/ai_core/tbe/:${PYTHONPATH}
 export DEVICE_MEMORY_CAPACITY=1073741824000
diff --git a/tests/st/auto_parallel/parallel_strategy_search.py b/tests/st/auto_parallel/parallel_strategy_search.py
new file mode 100644
index 00000000000..0631df0cefe
--- /dev/null
+++ b/tests/st/auto_parallel/parallel_strategy_search.py
@@ -0,0 +1,354 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import os
+import numpy as np
+
+from mindspore.communication.management import init
+from mindspore.communication.management import release
+from mindspore.communication.management import get_rank
+from mindspore.communication.management import get_group_size
+from mindspore.nn import Cell
+from mindspore.nn import Conv2d
+from mindspore.nn import ReLU
+from mindspore.nn import Dense
+from mindspore.nn import Softmax
+import mindspore.ops.operations as P
+from mindspore.train.serialization import load_param_into_net
+from mindspore.train.callback import CheckpointConfig
+from mindspore.train.callback import ModelCheckpoint
+from mindspore.train.serialization import load_checkpoint
+from mindspore.nn import Momentum
+from mindspore.nn import SoftmaxCrossEntropyWithLogits
+from mindspore.train import Model
+from mindspore.parallel import set_algo_parameters
+from mindspore.common.initializer import initializer
+from mindspore.common import dtype as mstype
+from mindspore import Tensor
+from mindspore.common.parameter import Parameter
+from mindspore import context
+from mindspore.context import ParallelMode
+
+context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
+
+
+def _count_unequal_element(data_expected, data_me, rtol, atol):
+    assert data_expected.shape == data_me.shape
+    total_count = len(data_expected.flatten())
+    error = np.abs(data_expected - data_me)
+    greater = np.greater(error, atol + np.abs(data_me) * rtol)
+    loss_count = np.count_nonzero(greater)
+    assert (loss_count / total_count) < rtol, \
+        "\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \
+            format(data_expected[greater], data_me[greater], error[greater])
+
+
+def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
+    if np.any(np.isnan(data_expected)):
+        assert np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan)
+    elif not np.allclose(data_expected, data_me, rtol, atol, equal_nan=equal_nan):
+        _count_unequal_element(data_expected, data_me, rtol, atol)
+    else:
+        assert True
+
+
+def clean_all_ckpt_files(folder_path):
+    if os.path.exists(folder_path):
+        for file_name in os.listdir(folder_path):
+            if file_name.endswith('.ckpt') or file_name.endswith('.meta'):
+                os.remove(os.path.join(folder_path, file_name))
+
+
+def find_newest_ckpt_file(folder_path):
+    ckpt_files = map(lambda f: os.path.join(folder_path, f),
+                     filter(lambda f: f.endswith('.ckpt'),
+                            os.listdir(folder_path)))
+    return max(ckpt_files, key=os.path.getctime)
+
+
+class FakeDataInitMode:
+    RandomInit = 0
+    OnesInit = 1
+    UniqueInit = 2
+    ZerosInit = 3
+
+
+class FakeData:
+    def __init__(self, size=1024, batch_size=32, image_size=(3, 224, 224),
+                 num_classes=10, random_offset=0, use_parallel=False,
+                 fakedata_mode=FakeDataInitMode.RandomInit):
+        self.size = size
+        self.rank_batch_size = batch_size
+        self.total_batch_size = self.rank_batch_size
+        self.random_offset = random_offset
+        self.image_size = image_size
+        self.num_classes = num_classes
+        self.rank_size = 1
+        self.rank_id = 0
+        self.batch_index = 0
+        self.image_data_type = np.float32
+        self.label_data_type = np.float32
+        self.is_onehot = True
+        self.fakedata_mode = fakedata_mode
+
+        if use_parallel is True:
+            init(backend_name='hccl')
+            self.rank_size = get_group_size()
+            self.rank_id = get_rank()
+
+        self.total_batch_size = self.rank_batch_size * self.rank_size
+
+        assert (self.size % self.total_batch_size) == 0
+
+        self.total_batch_data_size = (self.rank_size, self.rank_batch_size) + image_size
+
+    def get_dataset_size(self):
+        return int(self.size / self.total_batch_size)
+
+    def get_repeat_count(self):
+        return 1
+
+    def set_image_data_type(self, data_type):
+        self.image_data_type = data_type
+
+    def set_label_data_type(self, data_type):
+        self.label_data_type = data_type
+
+    def set_label_onehot(self, is_onehot=True):
+        self.is_onehot = is_onehot
+
+    def create_tuple_iterator(self, num_epochs=-1):
+        _ = num_epochs
+        return self
+
+    def __getitem__(self, batch_index):
+        if batch_index * self.total_batch_size >= len(self):
+            raise IndexError("{} index out of range".format(self.__class__.__name__))
+        rng_state = np.random.get_state()
+        np.random.seed(batch_index + self.random_offset)
+        if self.fakedata_mode == FakeDataInitMode.OnesInit:
+            img = np.ones(self.total_batch_data_size)
+        elif self.fakedata_mode == FakeDataInitMode.ZerosInit:
+            img = np.zeros(self.total_batch_data_size)
+        elif self.fakedata_mode == FakeDataInitMode.UniqueInit:
+            total_size = 1
+            for i in self.total_batch_data_size:
+                total_size = total_size * i
+            img = np.reshape(np.arange(total_size) * 0.0001, self.total_batch_data_size)
+        else:
+            img = np.random.randn(*self.total_batch_data_size)
+        target = np.random.randint(0, self.num_classes, size=(self.rank_size, self.rank_batch_size))
+        np.random.set_state(rng_state)
+        img = img[self.rank_id]
+        target = target[self.rank_id]
+        img_ret = img.astype(self.image_data_type)
+        target_ret = target.astype(self.label_data_type)
+        if self.is_onehot:
+            target_onehot = np.zeros(shape=(self.rank_batch_size, self.num_classes))
+            target_onehot[np.arange(self.rank_batch_size), target] = 1
+            target_ret = target_onehot.astype(self.label_data_type)
+        return Tensor(img_ret), Tensor(target_ret)
+
+    def __len__(self):
+        return self.size
+
+    def __iter__(self):
+        self.batch_index = 0
+        return self
+
+    def reset(self):
+        self.batch_index = 0
+
+    def __next__(self):
+        if self.batch_index * self.total_batch_size < len(self):
+            data = self[self.batch_index]
+            self.batch_index += 1
+            return data
+        raise StopIteration
+
+
+class ParallelStrategySearchNet(Cell):
+    def __init__(self, in_channel, out_channel, axis, input_shape, mul_size,
+                 test_size, prelu_size, transpose_b, matmul_size, num_class):
+        super().__init__()
+        mul_np = np.full(mul_size, 0.5, dtype=np.float32)
+        self.mul_weight = Parameter(Tensor(mul_np), name="mul_weight")
+        bias_np = np.full((12,), 7.1, dtype=np.float32)
+        self.bias = Parameter(Tensor(bias_np), name="bias")
+        prelu_np = np.full(prelu_size, 0.8, dtype=np.float32)
+        self.prelu_weight = Parameter(Tensor(prelu_np), name="prelu_weight")
+        matmul_np = np.full(matmul_size, 1.1, dtype=np.float32)
+        self.matmul_weight = Parameter(Tensor(matmul_np), name="matmul_weight")
+        self.mul = P.Mul()
+        self.conv = Conv2d(in_channels=in_channel, out_channels=out_channel,
+                           kernel_size=5, has_bias=True,
+                           weight_init='ones', bias_init='ones',
+                           pad_mode='valid')
+        self.scalar = 0.5
+        self.parameter = Parameter(
+            initializer(0.5, test_size, dtype=mstype.float32),
+            name='parameter')
+        self.tensor = Tensor(np.full(test_size, 0.05, dtype=np.float32))
+        self.softmax = Softmax(axis=axis)
+        self.relu = ReLU()
+        self.relu.relu.add_prim_attr("primitive_target", "CPU")
+        self.reshape = P.Reshape()
+        self.input_shape = input_shape
+        self.equal = P.Equal()
+        self.cast = P.Cast()
+        self.concat = P.Concat(axis=1)
+        self.reduce_sum = P.ReduceSum()
+        self.bias_add = P.BiasAdd()
+        self.cos = P.Cos()
+        self.prelu = P.PReLU()
+        self.matmul = P.MatMul(transpose_b=transpose_b)
+        self.l2norm = P.L2Normalize(axis=(1 - axis))
+        self.tensoradd = P.TensorAdd()
+        self.strided_slice = P.StridedSlice()
+        self.dense = Dense(in_channels=6,
+                           out_channels=num_class,
+                           weight_init='ones',
+                           bias_init='ones',
+                           has_bias=True)
+
+    def construct(self, inputs):
+        x = self.conv(inputs)
+        x = self.softmax(x)
+        x = self.relu(x)
+        x = self.mul(x, self.mul_weight)
+        x = self.reshape(x, self.input_shape)
+        y = self.parameter * self.tensor * self.scalar
+        z = self.equal(self.parameter, self.scalar)
+        z = self.cast(z, mstype.float16)
+        z = self.cast(z, mstype.float32)
+        x = self.concat((x, y, z))
+        x = self.reduce_sum(x, (2, 3))
+        x = self.bias_add(x, self.bias)
+        y = self.cos(x)
+        y = self.prelu(y, self.prelu_weight)
+        z = self.matmul(x, self.matmul_weight)
+        z = self.l2norm(z)
+        x = self.tensoradd(y, z)
+        x = self.strided_slice(x, (0, 0), (32, 6), (1, 1))
+        x = self.dense(x)
+        return x
+
+
+class ParallelStrategySearchFactory:
+    def __init__(self, standalone_mode_net, parallel_mode_net):
+        self.standalone_mode_net = standalone_mode_net
+        self.parallel_mode_net = parallel_mode_net
+        self.parallel_ckpt = None
+        self.standalone_ckpt = None
+        self.global_rank_id = None
+        self._set_parallel_env()
+        self._init_parallel()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        return
+
+    def __del__(self):
+        self._release_parallel()
+
+    def _set_parallel_env(self):
+        if 'RANK_ID' in os.environ:
+            self.global_rank_id = int(os.environ['RANK_ID'])
+
+    def _init_parallel(self):
+        self._init_parallel_flag = False
+        init(backend_name='hccl')
+        self._init_parallel_flag = True
+
+    def _release_parallel(self):
+        if self._init_parallel_flag:
+            release()
+
+    def _model_train_and_save_ckpt(self, net, dataset, epoch):
+        self.opt = Momentum(learning_rate=0.01, momentum=0.9, params=net.get_parameters())
+        self.loss_fn = SoftmaxCrossEntropyWithLogits(reduction='mean')
+        self.model = Model(network=net,
+                           loss_fn=self.loss_fn,
+                           optimizer=self.opt)
+        ckpt_config = CheckpointConfig(keep_checkpoint_max=1)
+        ckpt_path = './rank_{}_ckpt'.format(self.global_rank_id)
+        ckpt_callback = ModelCheckpoint(prefix='parallel', directory=ckpt_path,
+                                        config=ckpt_config)
+        clean_all_ckpt_files(ckpt_path)
+        self.model.train(epoch=epoch,
+                         train_dataset=dataset,
+                         callbacks=[ckpt_callback],
+                         dataset_sink_mode=False)
+        newest_ckpt_file = find_newest_ckpt_file(ckpt_path)
+        return load_checkpoint(newest_ckpt_file)
+
+    def mindspore_auto_parallel_impl(self, dataset, epoch, device_num):
+        parallel_mode_net = self.parallel_mode_net
+        set_algo_parameters(fully_use_devices=False)
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL,
+                                          device_num=device_num)
+        self.parallel_ckpt = self._model_train_and_save_ckpt(net=parallel_mode_net,
+                                                             dataset=dataset, epoch=epoch)
+        context.reset_auto_parallel_context()
+
+    def mindspore_standalone_impl(self, dataset, epoch):
+        standalone_mode_net = self.standalone_mode_net
+        context.reset_auto_parallel_context()
+        context.set_auto_parallel_context(parallel_mode=ParallelMode.STAND_ALONE)
+        self.standalone_ckpt = self._model_train_and_save_ckpt(net=standalone_mode_net,
+                                                               dataset=dataset, epoch=epoch)
+        context.reset_auto_parallel_context()
+
+    def checkpoint_cmp(self, inputs_np):
+        standalone_net = self.standalone_mode_net
+        load_param_into_net(standalone_net, self.standalone_ckpt)
+        standalone_out = standalone_net(Tensor(inputs_np))
+
+        parallel_net = self.standalone_mode_net
+        load_param_into_net(parallel_net, self.parallel_ckpt)
+        parallel_out = parallel_net(Tensor(inputs_np))
+        allclose_nparray(standalone_out.asnumpy(), parallel_out.asnumpy(),
+                         0.001, 0.001)
+
+
+def test_auto_parallel_strategy_search_axis_1_basic():
+    inputs_np = np.random.randn(32, 3, 224, 224).astype(np.float32)
+    standalone_mode_net = ParallelStrategySearchNet(in_channel=3,
+                                                    out_channel=8, axis=1, input_shape=(32, 4, 110, -1),
+                                                    mul_size=(32, 1, 220, 220), test_size=(32, 4, 110, 880),
+                                                    prelu_size=(1,), transpose_b=True, matmul_size=(1, 12),
+                                                    num_class=12)
+    context.reset_auto_parallel_context()
+    context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL)
+    parallel_mode_net = ParallelStrategySearchNet(in_channel=3,
+                                                  out_channel=8, axis=1, input_shape=(32, 4, 110, -1),
+                                                  mul_size=(32, 1, 220, 220), test_size=(32, 4, 110, 880),
+                                                  prelu_size=(1,), transpose_b=True, matmul_size=(1, 12),
+                                                  num_class=12)
+    parallel_mode_net.cos.shard(((2, 4),))
+    parallel_mode_net.matmul.shard(((1, 2), (1, 2)))
+    standalone_dataset = FakeData(size=128, batch_size=32,
+                                  image_size=(3, 224, 224), num_classes=12)
+    fact = ParallelStrategySearchFactory(standalone_mode_net=standalone_mode_net,
+                                         parallel_mode_net=parallel_mode_net)
+    fact.mindspore_standalone_impl(dataset=standalone_dataset, epoch=2)
+    parallel_dataset = FakeData(size=128, batch_size=4,
+                                image_size=(3, 224, 224), use_parallel=True,
+                                num_classes=12)
+    fact.mindspore_auto_parallel_impl(dataset=parallel_dataset,
+                                      epoch=2, device_num=8)
+    fact.checkpoint_cmp(inputs_np=inputs_np)
diff --git a/tests/st/auto_parallel/run_parallel_strategy_search.sh b/tests/st/auto_parallel/run_parallel_strategy_search.sh
new file mode 100644
index 00000000000..38a4814ca03
--- /dev/null
+++ b/tests/st/auto_parallel/run_parallel_strategy_search.sh
@@ -0,0 +1,52 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+set -e
+BASE_PATH=$(cd "$(dirname $0)"; pwd)
+CONFIG_PATH=/home/workspace/mindspore_config
+export DEVICE_NUM=8
+export RANK_SIZE=$DEVICE_NUM
+source ${BASE_PATH}/env.sh
+unset SLOG_PRINT_TO_STDOUT
+export MINDSPORE_HCCL_CONFIG_PATH=$CONFIG_PATH/hccl/rank_table_${DEVICE_NUM}p.json
+export LD_LIBRARY_PATH=/usr/local/Ascend/opp/op_impl/built-in/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH}
+export ASCEND_OPP_PATH=/usr/local/Ascend/opp/
+
+process_pid=()
+for((i=0; i<$DEVICE_NUM; i++)); do
+    rm -rf ${BASE_PATH}/parallel_strategy_search${i}
+    mkdir ${BASE_PATH}/parallel_strategy_search${i}
+    cp -r ${BASE_PATH}/parallel_strategy_search.py  ${BASE_PATH}/parallel_strategy_search${i}/
+    cd ${BASE_PATH}/parallel_strategy_search${i}
+    export RANK_ID=${i}
+    export DEVICE_ID=${i}
+    echo "start training for device $i"
+    env > env$i.log
+    pytest -s -v parallel_strategy_search.py > parallel_strategy_search$i.log 2>&1 &
+    process_pid[${i}]=`echo $!`
+done
+
+for((i=0; i<${DEVICE_NUM}; i++)); do
+    wait ${process_pid[i]}
+    status=`echo $?`
+    if [ "${status}" != "0" ]; then
+        echo "[ERROR] test_parallel_strategy_search failed. status: ${status}"
+        exit 1
+    else
+        echo "[INFO] test_parallel_strategy_search success."
+    fi
+done
+
+exit 0
diff --git a/tests/st/auto_parallel/test_parallel_strategy_search.py b/tests/st/auto_parallel/test_parallel_strategy_search.py
new file mode 100644
index 00000000000..d46a39bd799
--- /dev/null
+++ b/tests/st/auto_parallel/test_parallel_strategy_search.py
@@ -0,0 +1,27 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# 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_single
+def test_parallel_strategy_search():
+    sh_path = os.path.split(os.path.realpath(__file__))[0]
+    ret = os.system(f"sh {sh_path}/run_parallel_strategy_search.sh")
+    os.system(f"grep -E 'ERROR|error' {sh_path}/parallel_strategy_search*/parallel_strategy_search*log -C 3")
+    assert ret == 0