From 4cd237eee4495ad838a312164d0d4b3e43be9232 Mon Sep 17 00:00:00 2001
From: ZPaC <zhoupeichen@huawei.com>
Date: Thu, 16 Apr 2020 09:56:43 +0800
Subject: [PATCH] Add GPU NCCL ci test cases.

---
 tests/st/nccl/test_nccl_all.py               | 44 ++++++++++++++++++++
 tests/st/nccl/test_nccl_all_reduce_op.py     |  2 +-
 tests/st/nccl/test_nccl_lenet.py             |  3 +-
 tests/st/nccl/test_nccl_reduce_scatter_op.py |  2 -
 4 files changed, 47 insertions(+), 4 deletions(-)
 create mode 100644 tests/st/nccl/test_nccl_all.py

diff --git a/tests/st/nccl/test_nccl_all.py b/tests/st/nccl/test_nccl_all.py
new file mode 100644
index 00000000000..99494bb7411
--- /dev/null
+++ b/tests/st/nccl/test_nccl_all.py
@@ -0,0 +1,44 @@
+# 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_x86_gpu_training
+@pytest.mark.env_single
+def test_nccl_lenet():
+    return_code = os.system("mpirun -n 8 pytest -s test_nccl_lenet.py")
+    assert(return_code == 0)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_single
+def test_nccl_all_reduce_op():
+    return_code = os.system("mpirun -n 8 pytest -s test_nccl_all_reduce_op.py")
+    assert(return_code == 0)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_single
+def test_nccl_all_gather_op():
+    return_code = os.system("mpirun -n 8 pytest -s test_nccl_all_gather_op.py")
+    assert(return_code == 0)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_single
+def test_nccl_reduce_scatter_op():
+    return_code = os.system("mpirun -n 8 pytest -s test_nccl_reduce_scatter_op.py")
+    assert(return_code == 0)
diff --git a/tests/st/nccl/test_nccl_all_reduce_op.py b/tests/st/nccl/test_nccl_all_reduce_op.py
index 3ba8b219e48..7c2e5794637 100644
--- a/tests/st/nccl/test_nccl_all_reduce_op.py
+++ b/tests/st/nccl/test_nccl_all_reduce_op.py
@@ -20,7 +20,7 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.communication.management import init, NCCL_WORLD_COMM_GROUP, get_rank, get_group_size
-context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+context.set_context(mode=context.GRAPH_MODE, device_target='GPU', enable_dynamic_memory=False)
 
 init('nccl')
 rank = get_rank()
diff --git a/tests/st/nccl/test_nccl_lenet.py b/tests/st/nccl/test_nccl_lenet.py
index 5642603d42f..2aebc5da501 100644
--- a/tests/st/nccl/test_nccl_lenet.py
+++ b/tests/st/nccl/test_nccl_lenet.py
@@ -27,7 +27,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 init('nccl')
 
 epoch = 2
-total = 50000
+total = 5000
 batch_size = 32
 mini_batch = total // batch_size
 
@@ -94,3 +94,4 @@ def test_lenet_nccl():
     with open("ms_loss.txt", "w") as fo2:
         fo2.write("loss:")
         fo2.write(str(losses[-5:]))
+    assert(losses[-1] < 0.01)
diff --git a/tests/st/nccl/test_nccl_reduce_scatter_op.py b/tests/st/nccl/test_nccl_reduce_scatter_op.py
index af22c7690fd..32c1f31788c 100644
--- a/tests/st/nccl/test_nccl_reduce_scatter_op.py
+++ b/tests/st/nccl/test_nccl_reduce_scatter_op.py
@@ -62,8 +62,6 @@ def test_ReduceScatter():
     expect1 = np.ones([1, 1, 3, 3]).astype(np.float32) * 0.01 * size
     diff1 = output[1].asnumpy() - expect1
     error1 = np.ones(shape=expect1.shape) * 1.0e-5
-    print(expect1)
-    print(output[1])
     assert np.all(diff1 < error1)
     assert (output[1].shape() == expect1.shape)