2507 Add Horovod unit tests (#2519)

* [DLMED] add horovod tests

Signed-off-by: Nic Ma <[email protected]>

* [MONAI] python code formatting

Signed-off-by: monai-bot <[email protected]>

* [DLMED] skip min tests

Signed-off-by: Nic Ma <[email protected]>

* [DLMED] rename to skip auto CI

Signed-off-by: Nic Ma <[email protected]>

Co-authored-by: monai-bot <[email protected]>

Author: Nic-Ma

tests/hvd_evenly_divisible_all_gather.py

@@ -0,0 +1,57 @@
+# Copyright 2020 - 2021 MONAI Consortium
+# 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 horovod.torch as hvd
+import torch
+
+from monai.utils import evenly_divisible_all_gather
+
+
+class HvdEvenlyDivisibleAllGather:
+    def test_data(self):
+        # initialize Horovod
+        hvd.init()
+        if torch.cuda.is_available():
+            torch.cuda.set_device(hvd.local_rank())
+        self._run()
+
+    def _run(self):
+        if hvd.rank() == 0:
+            data1 = torch.tensor([[1, 2], [3, 4]])
+            data2 = torch.tensor([[1.0, 2.0]])
+            data3 = torch.tensor(7)
+
+        if hvd.rank() == 1:
+            data1 = torch.tensor([[5, 6]])
+            data2 = torch.tensor([[3.0, 4.0], [5.0, 6.0]])
+            data3 = torch.tensor(8)
+
+        result1 = evenly_divisible_all_gather(data=data1, concat=True)
+        torch.testing.assert_allclose(result1, torch.tensor([[1, 2], [3, 4], [5, 6]]))
+        result2 = evenly_divisible_all_gather(data=data2, concat=False)
+        for r, e in zip(result2, [torch.tensor([[1.0, 2.0]]), torch.tensor([[3.0, 4.0], [5.0, 6.0]])]):
+            torch.testing.assert_allclose(r, e)
+        result3 = evenly_divisible_all_gather(data=data3, concat=False)
+        for r in result3:
+            torch.testing.assert_allclose(r.ndimension(), 0)
+
+
+if __name__ == "__main__":
+    """
+    1. Install Horovod:
+    `HOROVOD_NCCL_INCLUDE=/usr/include HOROVOD_NCCL_LIB=/usr/lib/x86_64-linux-gnu HOROVOD_GPU_OPERATIONS=NCCL \
+    HOROVOD_NCCL_LINK=SHARED pip install --no-cache-dir horovod`
+
+    2. Execute on 2 GPUs in a single machine:
+    `horovodrun -np 2 python test_evenly_divisible_all_gather_hvd.py`
+
+    """
+    HvdEvenlyDivisibleAllGather().test_data()