add ipex multiprocessing test (#4724)

python/nano/test/pytorch/tests/test_plugin.py

@@ -26,6 +26,7 @@
 from bigdl.nano.pytorch import Trainer
 
 from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
+from test.pytorch.utils._train_torch_lightning import create_test_data_loader
 from test.pytorch.tests.test_lightning import ResNet18
 
 num_classes = 10
@@ -41,6 +42,8 @@ class TestPlugin(TestCase):
     optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
     data_loader = create_data_loader(data_dir, batch_size, num_workers,
                                      data_transform, subset=dataset_size)
+    test_data_loader = create_test_data_loader(data_dir, batch_size, num_workers,
+                                               data_transform, subset=dataset_size)
 
     def setUp(self):
         test_dir = os.path.dirname(__file__)
@@ -56,8 +59,8 @@ def test_trainer_subprocess_plugin(self):
         )
         trainer = Trainer(num_processes=2, distributed_backend="subprocess",
                           max_epochs=4)
-        trainer.fit(pl_model, self.data_loader, self.data_loader)
-        trainer.test(pl_model, self.data_loader)
+        trainer.fit(pl_model, self.data_loader, self.test_data_loader)
+        trainer.test(pl_model, self.test_data_loader)
 
 
 if __name__ == '__main__':

python/nano/test/pytorch/tests/test_plugin_ipex.py

@@ -0,0 +1,67 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# 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
+from unittest import TestCase
+
+import torch
+import torchmetrics
+from torch import nn
+
+from bigdl.nano.pytorch.lightning import LightningModuleFromTorch
+from bigdl.nano.pytorch import Trainer
+
+from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
+from test.pytorch.utils._train_torch_lightning import create_test_data_loader
+from test.pytorch.tests.test_lightning import ResNet18
+
+num_classes = 10
+batch_size = 32
+dataset_size = 256
+num_workers = 0
+data_dir = os.path.join(os.path.dirname(__file__), "../data")
+
+
+class TestPlugin(TestCase):
+    model = ResNet18(pretrained=False, include_top=False, freeze=True)
+    loss = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+    data_loader = create_data_loader(data_dir, batch_size, num_workers,
+                                     data_transform, subset=dataset_size)
+    test_data_loader = create_test_data_loader(data_dir, batch_size, num_workers,
+                                               data_transform, subset=dataset_size)
+
+    def setUp(self):
+        test_dir = os.path.dirname(__file__)
+        project_test_dir = os.path.abspath(
+            os.path.join(os.path.join(os.path.join(test_dir, ".."), ".."), "..")
+        )
+        os.environ['PYTHONPATH'] = project_test_dir
+
+    def test_trainer_subprocess_plugin(self):
+        pl_model = LightningModuleFromTorch(
+            self.model, self.loss, self.optimizer,
+            metrics=[torchmetrics.F1(num_classes), torchmetrics.Accuracy(num_classes=10)]
+        )
+        trainer = Trainer(num_processes=2, distributed_backend="subprocess",
+                          max_epochs=4, use_ipex=True)
+        trainer.fit(pl_model, self.data_loader, self.test_data_loader)
+        trainer.test(pl_model, self.test_data_loader)
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])