Add PyNative Hccl st

tests/st/pynative/data_parallel/test_pynative_hccl.py

@@ -0,0 +1,85 @@
+# 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.
+# ============================================================================
+
+"""test bert thor performance with 8p on mlperf dataset"""
+
+import os
+from multiprocessing import Process
+import pytest
+import numpy as np
+import mindspore.nn as nn
+from mindspore import Tensor
+import mindspore.dataset as dataset
+from mindspore import dtype as mstype
+from mindspore.ops import operations as P
+import mindspore.communication.management as D
+from mindspore import context
+from mindspore.context import ParallelMode
+
+MINDSPORE_HCCL_CONFIG_PATH = "/home/workspace/mindspore_config/hccl/rank_table_8p.json"
+
+np.random.seed(1)
+dataset.config.set_seed(1)
+os.environ['GLOG_v'] = str(2)
+
+class AllReduceNet(nn.Cell):
+    def __init__(self):
+        super(AllReduceNet, self).__init__()
+        self.all_reduce = P.AllReduce()
+
+    def construct(self, x):
+        return self.all_reduce(x)
+
+def train_allreduce_8p(device_id, device_num):
+    os.system("mkdir " + str(device_id))
+    os.chdir(str(device_id))
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend", device_id=device_id)
+    os.environ['MINDSPORE_HCCL_CONFIG_PATH'] = MINDSPORE_HCCL_CONFIG_PATH
+    os.environ['RANK_ID'] = str(device_id)
+    os.environ['RANK_SIZE'] = str(device_num)
+    D.init()
+    context.reset_auto_parallel_context()
+    context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
+                                      device_num=device_num)
+
+    net = AllReduceNet()
+    input_x = np.ones([32, 255, 255, 3]).astype(np.float32)
+    except_output = input_x * 8
+    output = net(Tensor(input_x, mstype.float32))
+    assert np.allclose(output.asnumpy(), except_output)
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_single
+def test_pynative_hccl_8p():
+    device_num = 8
+    process = []
+    for i in range(device_num):
+        device_id = i
+        process.append(Process(target=train_allreduce_8p, args=(device_id, device_num)))
+
+    for i in range(device_num):
+        process[i].start()
+
+    print("Waiting for all subprocesses done...")
+
+    for i in range(device_num):
+        process[i].join()
+
+    for i in range(device_num):
+        os.system("rm -rf " + str(i))
+
+    print("End training...")