chore: import upstream snapshot with attribution

test/collective/fleet/parallel_dygraph_sync_batch_norm.py

@@ -0,0 +1,101 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 numpy as np
+from legacy_test.test_dist_base import (
+    TestParallelDyGraphRunnerBase,
+    runtime_main,
+)
+
+import paddle
+from paddle.nn import Conv2D, SyncBatchNorm
+
+
+class TestLayer(paddle.nn.Layer):
+    def __init__(
+        self,
+        num_channels,
+        num_filters,
+        filter_size,
+        stride=1,
+        groups=1,
+        act=None,
+    ):
+        super().__init__()
+
+        self._conv = Conv2D(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            bias_attr=False,
+        )
+
+        self._sync_batch_norm = SyncBatchNorm(num_filters)
+
+        self._conv2 = Conv2D(
+            in_channels=num_filters,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            bias_attr=False,
+        )
+
+        self._sync_batch_norm2 = SyncBatchNorm(
+            num_filters, weight_attr=False, bias_attr=False
+        )
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._sync_batch_norm(y)
+        y = self._conv2(y)
+        y = self._sync_batch_norm2(y)
+
+        return y
+
+
+class TestSyncBatchNorm(TestParallelDyGraphRunnerBase):
+    def get_model(self):
+        model = TestLayer(3, 64, 7)
+        train_reader = paddle.batch(
+            paddle.dataset.flowers.test(use_xmap=False),
+            batch_size=32,
+            drop_last=True,
+        )
+        opt = paddle.optimizer.Adam(
+            learning_rate=1e-3, parameters=model.parameters()
+        )
+        return model, train_reader, opt
+
+    def run_one_loop(self, model, opt, data):
+        batch_size = len(data)
+        dy_x_data = np.array([x[0].reshape(3, 224, 224) for x in data]).astype(
+            'float32'
+        )
+        img = paddle.to_tensor(dy_x_data)
+        img.stop_gradient = False
+
+        out = model(img)
+
+        out = paddle.mean(out)
+
+        return out
+
+
+if __name__ == "__main__":
+    runtime_main(TestSyncBatchNorm)