chore: import upstream snapshot with attribution

python/paddle/distributed/launch/plugins/test.py

@@ -0,0 +1,105 @@
+# Copyright (c) 2022 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
+
+import paddle
+from paddle.distributed import fleet
+from paddle.io import DataLoader, Dataset
+from paddle.vision.models import ResNet
+from paddle.vision.models.resnet import BottleneckBlock
+
+base_lr = 0.1
+momentum_rate = 0.9
+l2_decay = 1e-4
+
+epoch = 3
+batch_num = 1
+batch_size = 1
+class_dim = 102
+
+
+# define a random dataset
+class RandomDataset(Dataset):
+    def __init__(self, num_samples):
+        self.num_samples = num_samples
+
+    def __getitem__(self, idx):
+        image = np.random.random([3, 224, 224]).astype('float32')
+        label = np.random.randint(0, class_dim - 1, (1,)).astype('int64')
+        return image, label
+
+    def __len__(self):
+        return self.num_samples
+
+
+def optimizer_setting(parameter_list=None):
+    optimizer = paddle.optimizer.Momentum(
+        learning_rate=base_lr,
+        momentum=momentum_rate,
+        weight_decay=paddle.regularizer.L2Decay(l2_decay),
+        parameters=parameter_list,
+    )
+    return optimizer
+
+
+def train_resnet():
+    fleet.init(is_collective=True)
+
+    resnet = ResNet(BottleneckBlock, 18, num_classes=class_dim)
+    optimizer = optimizer_setting(parameter_list=resnet.parameters())
+    optimizer = fleet.distributed_optimizer(optimizer)
+    resnet = fleet.distributed_model(resnet)
+
+    dataset = RandomDataset(batch_num * batch_size)
+    train_loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=True,
+        drop_last=True,
+        num_workers=2,
+    )
+
+    print("Distributed training start...")
+    for eop in range(epoch):
+        resnet.train()
+
+        for batch_id, data in enumerate(train_loader()):
+            img, label = data
+            label.stop_gradient = True
+
+            out = resnet(img)
+            loss = paddle.nn.functional.cross_entropy(input=out, label=label)
+            avg_loss = paddle.mean(x=loss)
+            acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
+            acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
+
+            avg_loss.backward()
+            optimizer.step()
+            resnet.clear_gradients()
+
+            print(
+                f"[Epoch {eop}, batch {batch_id}] loss: {avg_loss:.5f}, acc1: {acc_top1:.5f}, acc5: {acc_top5:.5f}"
+            )
+
+    print("Distributed training completed")
+
+
+if __name__ == '__main__':
+    import os
+
+    nnodes = os.getenv('PADDLE_NNODES')
+    cn = os.getenv('PADDLE_LOCAL_SIZE')
+    print(f"Prepare distributed training with {nnodes} nodes {cn} cards")
+    train_resnet()